1000007800010@unknown@formal@none@1@S@⌊δSpanish languageδ⌋@@@@1@2@@oe@26-8-2013
1000007800020@unknown@formal@none@1@S@⌊∗Spanish∗⌋ or ⌊∗Castilian∗⌋ (⌊/castellano/⌋) is an ⌊>Indo-European>⌋, ⌊>Romance language>⌋ that originated in northern ⌊>Spain>⌋, and gradually spread in the ⌊>Kingdom of Castile>⌋ and evolved into the principal language of government and trade.@@@@1@32@@oe@26-8-2013
1000007800030@unknown@formal@none@1@S@It was taken to ⌊>Africa>⌋, the ⌊>Americas>⌋, and ⌊>Asia Pacific>⌋ with the expansion of the ⌊>Spanish Empire>⌋ between the fifteenth and nineteenth centuries.@@@@1@23@@oe@26-8-2013
1000007800040@unknown@formal@none@1@S@Today, between 322 and 400 million people speak Spanish as a native language, making it the world's second most-spoken language by native speakers (after ⌊>Mandarin Chinese>⌋).@@@@1@26@@oe@26-8-2013
1000007800050@unknown@formal@none@1@S@⌊=Hispanosphere¦2=⌋@@@@1@1@@oe@26-8-2013
1000007800060@unknown@formal@none@1@S@It is estimated that the combined total of native and non-native Spanish speakers is approximately 500 million, likely making it the third most spoken language by total number of speakers (after ⌊>English>⌋ and ⌊>Chinese>⌋).@@@@1@34@@oe@26-8-2013
1000007800070@unknown@formal@none@1@S@Today, Spanish is an official language of Spain, most ⌊>Latin American>⌋ countries, and ⌊>Equatorial Guinea>⌋; 21 nations speak it as their primary language.@@@@1@23@@oe@26-8-2013
1000007800080@unknown@formal@none@1@S@Spanish also is one of ⌊>six official languages>⌋ of the ⌊>United Nations>⌋.@@@@1@12@@oe@26-8-2013
1000007800090@unknown@formal@none@1@S@⌊>Mexico>⌋ has the world's largest Spanish-speaking population, and Spanish is the second most-widely spoken language in the ⌊>United States>⌋ and the most popular studied foreign language in ⌊>U.S.>⌋ schools and universities.@@@@1@31@@oe@26-8-2013
1000007800100@unknown@formal@none@1@S@⌊>Global internet usage>⌋ statistics for 2007 show Spanish as the third most commonly used language on the Internet, after English and ⌊>Chinese>⌋.@@@@1@22@@oe@26-8-2013
1000007800110@unknown@formal@none@1@S@⌊=Naming and origin¦2=⌋@@@@1@3@@oe@26-8-2013
1000007800120@unknown@formal@none@1@S@Spaniards tend to call this language ⌊λ⌊∗⌊/español/⌋∗⌋¦es¦⌊∗⌊/español/⌋∗⌋¦Langλ⌋ (Spanish) when contrasting it with languages of other states, such as ⌊>French>⌋ and ⌊>English>⌋, but call it ⌊λ⌊∗⌊/castellano/⌋∗⌋¦es¦⌊∗⌊/castellano/⌋∗⌋¦Langλ⌋ (Castilian), that is, the language of the ⌊>Castile>⌋ region, when contrasting it with other ⌊>languages spoken in Spain>⌋ such as ⌊>Galician>⌋, ⌊>Basque>⌋, and ⌊>Catalan>⌋.@@@@1@49@@oe@26-8-2013
1000007800130@unknown@formal@none@1@S@This reasoning also holds true for the language's preferred name in some ⌊>Hispanic America>⌋n countries.@@@@1@15@@oe@26-8-2013
1000007800140@unknown@formal@none@1@S@In this manner, the ⌊>Spanish Constitution of 1978>⌋ uses the term ⌊λ⌊/castellano/⌋¦es¦⌊/castellano/⌋¦Langλ⌋ to define the ⌊>official language>⌋ of the whole Spanish State, as opposed to ⌊λ⌊/las demás lenguas españolas/⌋¦es¦⌊/las demás lenguas españolas/⌋¦Langλ⌋ (lit. ⌊/the other Spanish languages/⌋).@@@@1@37@@oe@26-8-2013
1000007800150@unknown@formal@none@1@S@Article III reads as follows:@@@@1@5@@oe@26-8-2013
1000007800160@unknown@formal@none@1@S@⌊"⌊λ⌊/El castellano es la lengua española oficial del Estado./⌋¦es¦⌊/El castellano es la lengua española oficial del Estado./⌋¦Langλ⌋@@@@1@17@@oe@26-8-2013
1000007800170@unknown@formal@none@1@S@⌊λ⌊/(…) Las demás lenguas españolas serán también oficiales en las respectivas Comunidades Autónomas…/⌋¦es¦⌊/(…) Las demás lenguas españolas serán también oficiales en las respectivas Comunidades Autónomas…/⌋¦Langλ⌋@@@@1@25@@oe@26-8-2013
1000007800180@unknown@formal@none@1@S@Castilian is the official Spanish language of the State.@@@@1@9@@oe@26-8-2013
1000007800190@unknown@formal@none@1@S@(…) The other Spanish languages shall also be official in their respective Autonomous Communities…"⌋@@@@1@14@@oe@26-8-2013
1000007800200@unknown@formal@none@1@S@The name ⌊/castellano/⌋ is, however, widely used for the language as a whole in Latin America.@@@@1@16@@oe@26-8-2013
1000007800210@unknown@formal@none@1@S@Some Spanish speakers consider ⌊/⌊λcastellano¦es¦castellano¦Langλ⌋/⌋ a generic term with no political or ideological links, much as "Spanish" is in English.@@@@1@20@@oe@26-8-2013
1000007800220@unknown@formal@none@1@S@Often Latin Americans use it to differentiate their own variety of Spanish as opposed to the variety of Spanish spoken in Spain, or variety of Spanish which is considered as standard in the region.@@@@1@34@@oe@26-8-2013
1000007800230@unknown@formal@none@1@S@⌊=Classification and related languages¦2=⌋@@@@1@4@@oe@26-8-2013
1000007800240@unknown@formal@none@1@S@Spanish is closely related to the other ⌊>West Iberian>⌋ Romance languages: ⌊>Asturian>⌋ (⌊λ⌊/asturianu/⌋¦ast¦⌊/asturianu/⌋¦Langλ⌋), ⌊>Galician>⌋ (⌊λ⌊/galego/⌋¦gl¦⌊/galego/⌋¦Langλ⌋), ⌊>Ladino>⌋ (⌊λ⌊/dzhudezmo/spanyol/kasteyano/⌋¦lad¦⌊/dzhudezmo/spanyol/kasteyano/⌋¦Langλ⌋), and ⌊>Portuguese>⌋ (⌊λ⌊/português/⌋¦pt¦⌊/português/⌋¦Langλ⌋).@@@@1@20@@oe@26-8-2013
1000007800250@unknown@formal@none@1@S@Catalan, an ⌊>East Iberian language>⌋ which exhibits many ⌊>Gallo-Romance>⌋ traits, is more similar to the neighbouring ⌊>Occitan language>⌋ (⌊λ⌊/occitan/⌋¦oc¦⌊/occitan/⌋¦Langλ⌋) than to Spanish, or indeed than Spanish and Portuguese are to each other.@@@@1@32@@oe@26-8-2013
1000007800260@unknown@formal@none@1@S@Spanish and Portuguese share similar grammars and vocabulary as well as a common history of ⌊>Arabic influence>⌋ while a great part of the peninsula was under ⌊>Islamic rule>⌋ (both languages expanded over ⌊>Islamic territories>⌋).@@@@1@34@@oe@26-8-2013
1000007800270@unknown@formal@none@1@S@Their ⌊>lexical similarity>⌋ has been estimated as 89%.@@@@1@8@@oe@26-8-2013
1000007800280@unknown@formal@none@1@S@See ⌊>Differences between Spanish and Portuguese>⌋ for further information.@@@@1@9@@oe@26-8-2013
1000007800290@unknown@formal@none@1@S@⌊=Ladino¦3=⌋@@@@1@1@@oe@26-8-2013
1000007800300@unknown@formal@none@1@S@Ladino, which is essentially medieval Spanish and closer to modern Spanish than any other language, is spoken by many descendants of the ⌊>Sephardi Jews>⌋ who were ⌊>expelled from Spain in the 15th century>⌋.@@@@1@33@@oe@26-8-2013
1000007800310@unknown@formal@none@1@S@Ladino speakers are currently almost exclusively ⌊>Sephardi>⌋ Jews, with family roots in Turkey, Greece or the Balkans: current speakers mostly live in Israel and Turkey, with a few pockets in Latin America.@@@@1@32@@oe@26-8-2013
1000007800320@unknown@formal@none@1@S@It lacks the ⌊>Native American vocabulary>⌋ which was influential during the ⌊>Spanish colonial period>⌋, and it retains many archaic features which have since been lost in standard Spanish.@@@@1@28@@oe@26-8-2013
1000007800330@unknown@formal@none@1@S@It contains, however, other vocabulary which is not found in standard Castilian, including vocabulary from ⌊>Hebrew>⌋, some French, Greek and ⌊>Turkish>⌋, and other languages spoken where the Sephardim settled.@@@@1@29@@oe@26-8-2013
1000007800340@unknown@formal@none@1@S@Ladino is in serious danger of extinction because many native speakers today are elderly as well as elderly ⌊/olim/⌋ (immigrants to ⌊>Israel>⌋) who have not transmitted the language to their children or grandchildren.@@@@1@33@@oe@26-8-2013
1000007800350@unknown@formal@none@1@S@However, it is experiencing a minor revival among Sephardi communities, especially in music.@@@@1@13@@oe@26-8-2013
1000007800360@unknown@formal@none@1@S@In the case of the Latin American communities, the danger of extinction is also due to the risk of assimilation by modern Castilian.@@@@1@23@@oe@26-8-2013
1000007800370@unknown@formal@none@1@S@A related dialect is ⌊>Haketia>⌋, the Judaeo-Spanish of northern Morocco.@@@@1@10@@oe@26-8-2013
1000007800380@unknown@formal@none@1@S@This too tended to assimilate with modern Spanish, during the Spanish occupation of the region.@@@@1@15@@oe@26-8-2013
1000007800390@unknown@formal@none@1@S@⌊=Vocabulary comparison¦3=⌋@@@@1@2@@oe@26-8-2013
1000007800400@unknown@formal@none@1@S@Spanish and ⌊>Italian>⌋ share a very similar phonological system.@@@@1@9@@oe@26-8-2013
1000007800410@unknown@formal@none@1@S@At present, the ⌊>lexical similarity>⌋ with Italian is estimated at 82%.@@@@1@11@@oe@26-8-2013
1000007800420@unknown@formal@none@1@S@As a result, Spanish and Italian are mutually intelligible to various degrees.@@@@1@12@@oe@26-8-2013
1000007800430@unknown@formal@none@1@S@The lexical similarity with ⌊>Portuguese>⌋ is greater, 89%, but the vagaries of Portuguese pronunciation make it less easily understood by Hispanophones than Italian.@@@@1@23@@oe@26-8-2013
1000007800440@unknown@formal@none@1@S@⌊>Mutual intelligibility>⌋ between Spanish and ⌊>French>⌋ or ⌊>Romanian>⌋ is even lower (lexical similarity being respectively 75% and 71%): comprehension of Spanish by French speakers who have not studied the language is as low as an estimated 45% - the same as of English.@@@@1@43@@oe@26-8-2013
1000007800450@unknown@formal@none@1@S@The common features of the writing systems of the Romance languages allow for a greater amount of interlingual reading comprehension than oral communication would.@@@@1@24@@oe@26-8-2013
1000007800460@unknown@formal@none@1@S@⌊↓1. also ⌊λ↓⌋⌊/⌊↓nós outros↓⌋/⌋⌊↓¦pt¦↓⌋⌊/⌊↓nós outros↓⌋/⌋⌊↓¦Langλ⌋ in early modern Portuguese (e.g. ↓⌋⌊/⌊↓⌊>The Lusiads>⌋↓⌋/⌋⌊↓)↓⌋@@@@1@12@@oe@26-8-2013
1000007800470@unknown@formal@none@1@S@⌊↓2. ⌊λ↓⌋⌊/⌊↓noi ↓⌋/⌋⌊∗⌊/⌊↓altri↓⌋/⌋∗⌋⌊↓¦it¦↓⌋⌊/⌊↓noi ↓⌋/⌋⌊∗⌊/⌊↓altri↓⌋/⌋∗⌋⌊↓¦Langλ⌋ in Southern ↓⌋⌊>⌊↓Italian dialects and languages↓⌋>⌋@@@@1@10@@oe@26-8-2013
1000007800480@unknown@formal@none@1@S@⌊↓3. Alternatively ⌊λ↓⌋⌊/⌊↓nous ↓⌋/⌋⌊∗⌊/⌊↓autres↓⌋/⌋∗⌋⌊↓¦fr¦↓⌋⌊/⌊↓nous ↓⌋/⌋⌊∗⌊/⌊↓autres↓⌋/⌋∗⌋⌊↓¦Langλ⌋↓⌋@@@@1@5@@oe@26-8-2013
1000007800490@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000007800500@unknown@formal@none@1@S@Spanish evolved from ⌊>Vulgar Latin>⌋, with major ⌊>influences from Arabic>⌋ in vocabulary during the ⌊>Andalusian>⌋ period and minor surviving influences from ⌊>Basque>⌋ and ⌊>Celtiberian>⌋, as well as ⌊>Germanic languages>⌋ via the ⌊>Visigoths>⌋.@@@@1@32@@oe@26-8-2013
1000007800510@unknown@formal@none@1@S@Spanish developed along the remote cross road strips among the ⌊>Alava>⌋, ⌊>Cantabria>⌋, ⌊>Burgos>⌋, ⌊>Soria>⌋ and ⌊>La Rioja>⌋ provinces of Northern Spain, as a strongly innovative and differing variant from its nearest cousin, ⌊>Leonese speech>⌋, with a higher degree of Basque influence in these regions (see ⌊>Iberian Romance languages>⌋).@@@@1@48@@oe@26-8-2013
1000007800520@unknown@formal@none@1@S@Typical features of Spanish diachronical ⌊>phonology>⌋ include ⌊>lenition>⌋ (Latin ⌊λ⌊/vita/⌋¦la¦⌊/vita/⌋¦Langλ⌋, Spanish ⌊λ⌊/vida/⌋¦es¦⌊/vida/⌋¦Langλ⌋), ⌊>palatalization>⌋ (Latin ⌊λ⌊/annum/⌋¦la¦⌊/annum/⌋¦Langλ⌋, Spanish ⌊λ⌊/año/⌋¦es¦⌊/año/⌋¦Langλ⌋, and Latin ⌊λ⌊/anellum/⌋¦la¦⌊/anellum/⌋¦Langλ⌋, Spanish ⌊λ⌊/anillo/⌋¦es¦⌊/anillo/⌋¦Langλ⌋) and ⌊>diphthong>⌋ation (⌊>stem>⌋-changing) of short ⌊/e/⌋ and ⌊/o/⌋ from Vulgar Latin (Latin ⌊λ⌊/terra/⌋¦la¦⌊/terra/⌋¦Langλ⌋, Spanish ⌊λ⌊/tierra/⌋¦es¦⌊/tierra/⌋¦Langλ⌋; Latin ⌊λ⌊/novus/⌋¦la¦⌊/novus/⌋¦Langλ⌋, Spanish ⌊λ⌊/nuevo/⌋¦es¦⌊/nuevo/⌋¦Langλ⌋).@@@@1@41@@oe@26-8-2013
1000007800530@unknown@formal@none@1@S@Similar phenomena can be found in other Romance languages as well.@@@@1@11@@oe@26-8-2013
1000007800540@unknown@formal@none@1@S@During the ⌊λ⌊/⌊>Reconquista>⌋/⌋¦es¦⌊/⌊>Reconquista>⌋/⌋¦Langλ⌋, this northern dialect from ⌊>Cantabria>⌋ was carried south, and remains a ⌊>minority language>⌋ in the northern coastal ⌊>Morocco>⌋.@@@@1@21@@oe@26-8-2013
1000007800550@unknown@formal@none@1@S@The first Latin-to-Spanish grammar (⌊λ⌊/Gramática de la Lengua Castellana/⌋¦es¦⌊/Gramática de la Lengua Castellana/⌋¦Langλ⌋) was written in ⌊>Salamanca>⌋, Spain, in 1492, by ⌊>Elio Antonio de Nebrija>⌋.@@@@1@25@@oe@26-8-2013
1000007800560@unknown@formal@none@1@S@When it was presented to ⌊>Isabel de Castilla>⌋, she asked, "What do I want a work like this for, if I already know the language?", to which he replied, "Your highness, the language is the instrument of the Empire."@@@@1@39@@oe@26-8-2013
1000007800570@unknown@formal@none@1@S@From the 16th century onwards, the language was taken to the ⌊>Americas>⌋ and the ⌊>Spanish East Indies>⌋ via ⌊>Spanish colonization>⌋.@@@@1@20@@oe@26-8-2013
1000007800580@unknown@formal@none@1@S@In the 20th century, Spanish was introduced to ⌊>Equatorial Guinea>⌋ and the ⌊>Western Sahara>⌋, the United States, such as in ⌊>Spanish Harlem>⌋, in ⌊>New York City>⌋, that had not been part of the Spanish Empire.@@@@1@35@@oe@26-8-2013
1000007800590@unknown@formal@none@1@S@For details on borrowed words and other external influences upon Spanish, see ⌊>Influences on the Spanish language>⌋.@@@@1@17@@oe@26-8-2013
1000007800600@unknown@formal@none@1@S@⌊=Characterization¦3=⌋@@@@1@1@@oe@26-8-2013
1000007800610@unknown@formal@none@1@S@A defining characteristic of Spanish was the ⌊>diphthong>⌋ization of the Latin short vowels ⌊/e/⌋ and ⌊/o/⌋ into ⌊/ie/⌋ and ⌊/ue/⌋, respectively, when they were stressed.@@@@1@25@@oe@26-8-2013
1000007800620@unknown@formal@none@1@S@Similar ⌊>sound changes>⌋ are found in other Romance languages, but in Spanish they were significant.@@@@1@15@@oe@26-8-2013
1000007800630@unknown@formal@none@1@S@Some examples:@@@@1@2@@oe@26-8-2013
1000007800640@unknown@formal@none@1@S@⌊•⌊#Lat. ⌊λ⌊/petra/⌋¦la¦⌊/petra/⌋¦Langλ⌋ > Sp. ⌊λ⌊/piedra/⌋¦es¦⌊/piedra/⌋¦Langλ⌋, It. ⌊λ⌊/pietra/⌋¦it¦⌊/pietra/⌋¦Langλ⌋, Fr. ⌊λ⌊/pierre/⌋¦fr¦⌊/pierre/⌋¦Langλ⌋, Rom. ⌊λ⌊/piatrǎ/⌋¦ro¦⌊/piatrǎ/⌋¦Langλ⌋, Port./Gal. ⌊λ⌊/pedra/⌋¦pt¦⌊/pedra/⌋¦Langλ⌋ "stone".#⌋@@@@1@14@@oe@26-8-2013
1000007800650@unknown@formal@none@1@S@⌊#Lat. ⌊λ⌊/moritur/⌋¦la¦⌊/moritur/⌋¦Langλ⌋ > Sp. ⌊λ⌊/muere/⌋¦es¦⌊/muere/⌋¦Langλ⌋, It. ⌊λ⌊/muore/⌋¦it¦⌊/muore/⌋¦Langλ⌋, Fr. ⌊λ⌊/meurt/⌋¦fr¦⌊/meurt/⌋¦Langλ⌋ / ⌊λ⌊/muert/⌋¦fr¦⌊/muert/⌋¦Langλ⌋, Rom. ⌊λ⌊/moare/⌋¦ro¦⌊/moare/⌋¦Langλ⌋, Port./Gal. ⌊λ⌊/morre/⌋¦pt¦⌊/morre/⌋¦Langλ⌋ "die".#⌋•⌋@@@@1@16@@oe@26-8-2013
1000007800660@unknown@formal@none@1@S@Peculiar to early Spanish (as in the ⌊>Gascon>⌋ dialect of Occitan, and possibly due to a Basque ⌊>substratum>⌋) was the mutation of Latin initial ⌊/f-/⌋ into ⌊/h-/⌋ whenever it was followed by a vowel that did not diphthongate.@@@@1@38@@oe@26-8-2013
1000007800670@unknown@formal@none@1@S@Compare for instance:@@@@1@3@@oe@26-8-2013
1000007800680@unknown@formal@none@1@S@⌊•⌊#Lat. ⌊λ⌊/filium/⌋¦la¦⌊/filium/⌋¦Langλ⌋ > It. ⌊λ⌊/figlio/⌋¦it¦⌊/figlio/⌋¦Langλ⌋, Port. ⌊λ⌊/filho/⌋¦pt¦⌊/filho/⌋¦Langλ⌋, Gal. ⌊λ⌊/fillo/⌋¦gl¦⌊/fillo/⌋¦Langλ⌋, Fr. ⌊λ⌊/fils/⌋¦fr¦⌊/fils/⌋¦Langλ⌋, Occitan ⌊λ⌊/filh/⌋¦oc¦⌊/filh/⌋¦Langλ⌋ (but Gascon ⌊λ⌊/hilh/⌋¦gsc¦⌊/hilh/⌋¦Langλ⌋) Sp. ⌊λ⌊/hijo/⌋¦es¦⌊/hijo/⌋¦Langλ⌋ (but Ladino ⌊λ⌊/fijo/⌋¦lad¦⌊/fijo/⌋¦Langλ⌋);#⌋@@@@1@21@@oe@26-8-2013
1000007800690@unknown@formal@none@1@S@⌊#Lat. ⌊λ⌊/fabulari/⌋¦la¦⌊/fabulari/⌋¦Langλ⌋ > Lad. ⌊λ⌊/favlar/⌋¦lad¦⌊/favlar/⌋¦Langλ⌋, Port./Gal. ⌊λ⌊/falar/⌋¦pt¦⌊/falar/⌋¦Langλ⌋, Sp. ⌊λ⌊/hablar/⌋¦es¦⌊/hablar/⌋¦Langλ⌋;#⌋@@@@1@9@@oe@26-8-2013
1000007800700@unknown@formal@none@1@S@⌊#but Lat. ⌊λ⌊/focum/⌋¦la¦⌊/focum/⌋¦Langλ⌋ > It. ⌊λ⌊/fuoco/⌋¦it¦⌊/fuoco/⌋¦Langλ⌋, Port./Gal. ⌊λ⌊/fogo/⌋¦pt¦⌊/fogo/⌋¦Langλ⌋, Sp./Lad. ⌊λ⌊/fuego/⌋¦es¦⌊/fuego/⌋¦Langλ⌋.#⌋•⌋@@@@1@10@@oe@26-8-2013
1000007800710@unknown@formal@none@1@S@Some ⌊>consonant cluster>⌋s of Latin also produced characteristically different results in these languages, for example:@@@@1@15@@oe@26-8-2013
1000007800720@unknown@formal@none@1@S@⌊•⌊#Lat. ⌊λ⌊/clamare/⌋¦la¦⌊/clamare/⌋¦Langλ⌋, acc. ⌊λ⌊/flammam/⌋¦la¦⌊/flammam/⌋¦Langλ⌋, ⌊λ⌊/plenum/⌋¦la¦⌊/plenum/⌋¦Langλ⌋ > Lad. ⌊λ⌊/lyamar/⌋¦lad¦⌊/lyamar/⌋¦Langλ⌋, ⌊λ⌊/flama/⌋¦lad¦⌊/flama/⌋¦Langλ⌋, ⌊λ⌊/pleno/⌋¦lad¦⌊/pleno/⌋¦Langλ⌋; Sp. ⌊λ⌊/llamar/⌋¦es¦⌊/llamar/⌋¦Langλ⌋, ⌊λ⌊/llama/⌋¦es¦⌊/llama/⌋¦Langλ⌋, ⌊λ⌊/lleno/⌋¦es¦⌊/lleno/⌋¦Langλ⌋.@@@@1@14@@oe@26-8-2013
1000007800730@unknown@formal@none@1@S@However, in Spanish there are also the forms ⌊λ⌊/clamar/⌋¦la¦⌊/clamar/⌋¦Langλ⌋, ⌊λ⌊/flama/⌋¦lad¦⌊/flama/⌋¦Langλ⌋, ⌊λ⌊/pleno/⌋¦lad¦⌊/pleno/⌋¦Langλ⌋; Port. ⌊λ⌊/chamar/⌋¦pt¦⌊/chamar/⌋¦Langλ⌋, ⌊λ⌊/chama/⌋¦pt¦⌊/chama/⌋¦Langλ⌋, ⌊λ⌊/cheio/⌋¦pt¦⌊/cheio/⌋¦Langλ⌋; Gal. ⌊λ⌊/chamar/⌋¦gl¦⌊/chamar/⌋¦Langλ⌋, ⌊λ⌊/chama/⌋¦gl¦⌊/chama/⌋¦Langλ⌋, ⌊λ⌊/cheo/⌋¦gl¦⌊/cheo/⌋¦Langλ⌋.#⌋@@@@1@19@@oe@26-8-2013
1000007800740@unknown@formal@none@1@S@⌊#Lat. acc. ⌊λ⌊/octo/⌋¦la¦⌊/octo/⌋¦Langλ⌋, ⌊λ⌊/noctem/⌋¦la¦⌊/noctem/⌋¦Langλ⌋, ⌊λ⌊/multum/⌋¦la¦⌊/multum/⌋¦Langλ⌋ > Lad. ⌊λ⌊/ocho/⌋¦lad¦⌊/ocho/⌋¦Langλ⌋, ⌊λ⌊/noche/⌋¦lad¦⌊/noche/⌋¦Langλ⌋, ⌊λ⌊/muncho/⌋¦lad¦⌊/muncho/⌋¦Langλ⌋; Sp. ⌊λ⌊/ocho/⌋¦es¦⌊/ocho/⌋¦Langλ⌋, ⌊λ⌊/noche/⌋¦es¦⌊/noche/⌋¦Langλ⌋, ⌊λ⌊/mucho/⌋¦es¦⌊/mucho/⌋¦Langλ⌋; Port. ⌊λ⌊/oito/⌋¦pt¦⌊/oito/⌋¦Langλ⌋, ⌊λ⌊/noite/⌋¦pt¦⌊/noite/⌋¦Langλ⌋, ⌊λ⌊/muito/⌋¦pt¦⌊/muito/⌋¦Langλ⌋; Gal. ⌊λ⌊/oito/⌋¦gl¦⌊/oito/⌋¦Langλ⌋, ⌊λ⌊/noite/⌋¦gl¦⌊/noite/⌋¦Langλ⌋, ⌊λ⌊/moito/⌋¦gl¦⌊/moito/⌋¦Langλ⌋.#⌋•⌋@@@@1@22@@oe@26-8-2013
1000007800750@unknown@formal@none@1@S@⌊=Geographic distribution¦2=⌋@@@@1@2@@oe@26-8-2013
1000007800760@unknown@formal@none@1@S@Spanish is one of the official languages of the ⌊>European Union>⌋, the ⌊>Organization of American States>⌋, the ⌊>Organization of Ibero-American States>⌋, the ⌊>United Nations>⌋, and the ⌊>Union of South American Nations>⌋.@@@@1@31@@oe@26-8-2013
1000007800770@unknown@formal@none@1@S@⌊=Europe¦3=⌋@@@@1@1@@oe@26-8-2013
1000007800780@unknown@formal@none@1@S@Spanish is an official language of Spain, the country for which it is named and from which it originated.@@@@1@19@@oe@26-8-2013
1000007800790@unknown@formal@none@1@S@It is also spoken in ⌊>Gibraltar>⌋, though English is the official language.@@@@1@12@@oe@26-8-2013
1000007800800@unknown@formal@none@1@S@Likewise, it is spoken in ⌊>Andorra>⌋ though ⌊>Catalan>⌋ is the official language.@@@@1@12@@oe@26-8-2013
1000007800810@unknown@formal@none@1@S@It is also spoken by small communities in other European countries, such as the ⌊>United Kingdom>⌋, ⌊>France>⌋, and ⌊>Germany>⌋.@@@@1@19@@oe@26-8-2013
1000007800820@unknown@formal@none@1@S@Spanish is an official language of the ⌊>European Union>⌋.@@@@1@9@@oe@26-8-2013
1000007800830@unknown@formal@none@1@S@In Switzerland, Spanish is the ⌊>mother tongue>⌋ of 1.7% of the population, representing the first minority after the 4 official languages of the country.@@@@1@24@@oe@26-8-2013
1000007800840@unknown@formal@none@1@S@⌊=The Americas¦3=⌋@@@@1@2@@oe@26-8-2013
1000007800850@unknown@formal@none@1@S@⌊=Latin America¦4=⌋@@@@1@2@@oe@26-8-2013
1000007800860@unknown@formal@none@1@S@Most Spanish speakers are in ⌊>Latin America>⌋; of most countries with the most Spanish speakers, only ⌊>Spain>⌋ is outside of the ⌊>Americas>⌋.@@@@1@22@@oe@26-8-2013
1000007800870@unknown@formal@none@1@S@⌊>Mexico>⌋ has most of the world's native speakers.@@@@1@8@@oe@26-8-2013
1000007800880@unknown@formal@none@1@S@Nationally, Spanish is the official language of ⌊>Argentina>⌋, ⌊>Bolivia>⌋ (co-official ⌊>Quechua>⌋ and ⌊>Aymara>⌋), ⌊>Chile>⌋, ⌊>Colombia>⌋, ⌊>Costa Rica>⌋, ⌊>Cuba>⌋, ⌊>Dominican Republic>⌋, ⌊>Ecuador>⌋, ⌊>El Salvador>⌋, ⌊>Guatemala>⌋, ⌊>Honduras>⌋, ⌊>Mexico>⌋ , ⌊>Nicaragua>⌋, ⌊>Panama>⌋, ⌊>Paraguay>⌋ (co-official ⌊>Guaraní>⌋), ⌊>Peru>⌋ (co-official ⌊>Quechua>⌋ and, in some regions, ⌊>Aymara>⌋), ⌊>Uruguay>⌋, and ⌊>Venezuela>⌋.@@@@1@43@@oe@26-8-2013
1000007800890@unknown@formal@none@1@S@Spanish is also the official language (co-official with ⌊>English>⌋) in the U.S. commonwealth of ⌊>Puerto Rico>⌋.@@@@1@16@@oe@26-8-2013
1000007800900@unknown@formal@none@1@S@Spanish has no official recognition in the former ⌊>British colony>⌋ of ⌊>Belize>⌋; however, per the 2000 census, it is spoken by 43% of the population.@@@@1@25@@oe@26-8-2013
1000007800910@unknown@formal@none@1@S@Mainly, it is spoken by Hispanic descendants who remained in the region since the 17th century; however, English is the official language.@@@@1@22@@oe@26-8-2013
1000007800920@unknown@formal@none@1@S@Spain colonized ⌊>Trinidad and Tobago>⌋ first in ⌊>1498>⌋, leaving the ⌊>Carib>⌋ people the Spanish language.@@@@1@15@@oe@26-8-2013
1000007800930@unknown@formal@none@1@S@Also the ⌊>Cocoa Panyol>⌋s, laborers from Venezuela, took their culture and language with them; they are accredited with the music of "⌊>Parang>⌋" ("⌊>Parranda>⌋") on the island.@@@@1@26@@oe@26-8-2013
1000007800940@unknown@formal@none@1@S@Because of Trinidad's location on the South American coast, the country is much influenced by its Spanish-speaking neighbors.@@@@1@18@@oe@26-8-2013
1000007800950@unknown@formal@none@1@S@A recent census shows that more than 1,500 inhabitants speak Spanish.@@@@1@11@@oe@26-8-2013
1000007800960@unknown@formal@none@1@S@In 2004, the government launched the ⌊/Spanish as a First Foreign Language/⌋ (SAFFL) initiative in March 2005.@@@@1@17@@oe@26-8-2013
1000007800970@unknown@formal@none@1@S@Government regulations require Spanish to be taught, beginning in primary school, while thirty percent of public employees are to be linguistically competent within five years.@@@@1@25@@oe@26-8-2013
1000007800980@unknown@formal@none@1@S@The government also announced that Spanish will be the country's second official language by ⌊>2020>⌋, beside English.@@@@1@17@@oe@26-8-2013
1000007800990@unknown@formal@none@1@S@Spanish is important in ⌊>Brazil>⌋ because of its proximity to and increased trade with its Spanish-speaking neighbors; for example, as a member of the ⌊>Mercosur>⌋ trading bloc.@@@@1@27@@oe@26-8-2013
1000007801000@unknown@formal@none@1@S@In 2005, the ⌊>National Congress of Brazil>⌋ approved a bill, signed into law by the ⌊>President>⌋, making Spanish available as a foreign language in secondary schools.@@@@1@26@@oe@26-8-2013
1000007801010@unknown@formal@none@1@S@In many border towns and villages (especially on the Uruguayan-Brazilian border), a ⌊>mixed language>⌋ known as ⌊>Portuñol>⌋ is spoken.@@@@1@19@@oe@26-8-2013
1000007801020@unknown@formal@none@1@S@⌊=United States¦4=⌋@@@@1@2@@oe@26-8-2013
1000007801030@unknown@formal@none@1@S@In the 2006 census, 44.3 million people of the U.S. population were ⌊>Hispanic>⌋ or ⌊>Latino>⌋ by origin; 34 million people, 12.2 percent, of the population older than 5 years speak Spanish at home.@@@@1@33@@oe@26-8-2013
1000007801040@unknown@formal@none@1@S@Spanish has a ⌊>long history in the United States>⌋ (many south-western states were part of Mexico and Spain), and it recently has been revitalized by much immigration from Latin America.@@@@1@30@@oe@26-8-2013
1000007801050@unknown@formal@none@1@S@Spanish is the most widely taught foreign language in the country.@@@@1@11@@oe@26-8-2013
1000007801060@unknown@formal@none@1@S@Although the United States has no formally designated "official languages," Spanish is formally recognized at the state level beside English; in the U.S. state of ⌊>New Mexico>⌋, 30 per cent of the population speak it.@@@@1@35@@oe@26-8-2013
1000007801070@unknown@formal@none@1@S@It also has strong influence in metropolitan areas such as Los Angeles, Miami and New York City.@@@@1@17@@oe@26-8-2013
1000007801080@unknown@formal@none@1@S@Spanish is the dominant spoken language in ⌊>Puerto Rico>⌋, a U.S. territory.@@@@1@12@@oe@26-8-2013
1000007801090@unknown@formal@none@1@S@In total, the U.S. has the world's fifth-largest Spanish-speaking population.@@@@1@10@@oe@26-8-2013
1000007801100@unknown@formal@none@1@S@⌊=Asia¦3=⌋@@@@1@1@@oe@26-8-2013
1000007801110@unknown@formal@none@1@S@Spanish was an official language of the ⌊>Philippines>⌋ but was never spoken by a majority of the population.@@@@1@18@@oe@26-8-2013
1000007801120@unknown@formal@none@1@S@Movements for most of the masses to learn the language were started but were stopped by the friars.@@@@1@18@@oe@26-8-2013
1000007801130@unknown@formal@none@1@S@Its importance fell in the first half of the 20th century following the U.S. occupation and administration of the islands.@@@@1@20@@oe@26-8-2013
1000007801140@unknown@formal@none@1@S@The introduction of the English language in the Philippine government system put an end to the use of Spanish as the official language.@@@@1@23@@oe@26-8-2013
1000007801150@unknown@formal@none@1@S@The language lost its official status in 1973 during the ⌊>Ferdinand Marcos>⌋ administration.@@@@1@13@@oe@26-8-2013
1000007801160@unknown@formal@none@1@S@Spanish is spoken mainly by small communities of Filipino-born Spaniards, Latin Americans, and Filipino ⌊>mestizo>⌋s (mixed race), descendants of the early colonial Spanish settlers.@@@@1@24@@oe@26-8-2013
1000007801170@unknown@formal@none@1@S@Throughout the 20th century, the Spanish language has declined in importance compared to English and ⌊>Tagalog>⌋.@@@@1@16@@oe@26-8-2013
1000007801180@unknown@formal@none@1@S@According to the 1990 Philippine census, there were 2,658 native speakers of Spanish.@@@@1@13@@oe@26-8-2013
1000007801190@unknown@formal@none@1@S@No figures were provided during the 1995 and 2000 censuses; however, figures for 2000 did specify there were over 600,000 native speakers of ⌊>Chavacano>⌋, a Spanish based ⌊>creole>⌋ language spoken in ⌊>Cavite>⌋ and ⌊>Zamboanga>⌋.@@@@1@34@@oe@26-8-2013
1000007801200@unknown@formal@none@1@S@Some other sources put the number of Spanish speakers in the Philippines around two to three million; however, these sources are disputed.@@@@1@22@@oe@26-8-2013
1000007801210@unknown@formal@none@1@S@In Tagalog, there are 4,000 Spanish adopted words and around 6,000 Spanish adopted words in Visayan and other Philippine languages as well.@@@@1@22@@oe@26-8-2013
1000007801220@unknown@formal@none@1@S@Today Spanish is offered as a foreign language in Philippines schools and universities.@@@@1@13@@oe@26-8-2013
1000007801230@unknown@formal@none@1@S@⌊=Africa¦3=⌋@@@@1@1@@oe@26-8-2013
1000007801240@unknown@formal@none@1@S@In Africa, Spanish is official in the UN-recognised but Moroccan-occupied ⌊>Western Sahara>⌋ (co-official ⌊>Arabic>⌋) and ⌊>Equatorial Guinea>⌋ (co-official ⌊>French>⌋ and ⌊>Portuguese>⌋).@@@@1@21@@oe@26-8-2013
1000007801250@unknown@formal@none@1@S@Today, nearly 200,000 refugee Sahrawis are able to read and write in Spanish, and several thousands have received ⌊>university>⌋ education in foreign countries as part of aid packages (mainly ⌊>Cuba>⌋ and ⌊>Spain>⌋).@@@@1@32@@oe@26-8-2013
1000007801260@unknown@formal@none@1@S@In Equatorial Guinea, Spanish is the predominant language when counting native and non-native speakers (around 500,000 people), while ⌊>Fang>⌋ is the most spoken language by a number of native speakers.@@@@1@30@@oe@26-8-2013
1000007801270@unknown@formal@none@1@S@It is also spoken in the Spanish cities in ⌊>continental North Africa>⌋ (⌊>Ceuta>⌋ and ⌊>Melilla>⌋) and in the autonomous community of ⌊>Canary Islands>⌋ (143,000 and 1,995,833 people, respectively).@@@@1@28@@oe@26-8-2013
1000007801280@unknown@formal@none@1@S@Within Northern Morocco, a former ⌊>Franco-Spanish protectorate>⌋ that is also geographically close to Spain, approximately 20,000 people speak Spanish.@@@@1@19@@oe@26-8-2013
1000007801290@unknown@formal@none@1@S@It is spoken by some communities of ⌊>Angola>⌋, because of the Cuban influence from the ⌊>Cold War>⌋, and in ⌊>Nigeria>⌋ by the descendants of ⌊>Afro-Cuban>⌋ ex-slaves.@@@@1@26@@oe@26-8-2013
1000007801300@unknown@formal@none@1@S@In ⌊>Côte d'Ivoire>⌋ and ⌊>Senegal>⌋, Spanish can be learned as a second foreign language in the public education system.@@@@1@19@@oe@26-8-2013
1000007801310@unknown@formal@none@1@S@In 2008, ⌊>Cervantes Institute>⌋s centers will be opened in ⌊>Lagos>⌋ and ⌊>Johannesburg>⌋, the first one in the ⌊>Sub-Saharan Africa>⌋@@@@1@19@@oe@26-8-2013
1000007801320@unknown@formal@none@1@S@⌊=Oceania¦3=⌋@@@@1@1@@oe@26-8-2013
1000007801330@unknown@formal@none@1@S@Among the countries and territories in ⌊>Oceania>⌋, Spanish is also spoken in ⌊>Easter Island>⌋, a territorial possession of Chile.@@@@1@19@@oe@26-8-2013
1000007801340@unknown@formal@none@1@S@According to the 2001 census, there are approximately 95,000 speakers of Spanish in Australia, 44,000 of which live in Greater Sydney , where the older ⌊>Mexican>⌋, ⌊>Colombian>⌋, and ⌊>Spanish>⌋ populations and newer ⌊>Argentine>⌋, Salvadoran and ⌊>Uruguyan>⌋ communities live.@@@@1@38@@oe@26-8-2013
1000007801350@unknown@formal@none@1@S@The island nations of ⌊>Guam>⌋, ⌊>Palau>⌋, ⌊>Northern Marianas>⌋, ⌊>Marshall Islands>⌋ and ⌊>Federated States of Micronesia>⌋ all once had Spanish speakers, since ⌊>Marianas>⌋ and ⌊>Caroline Islands>⌋ were Spanish colonial possessions until late 19th century (see ⌊>Spanish-American War>⌋), but Spanish has since been forgotten.@@@@1@42@@oe@26-8-2013
1000007801360@unknown@formal@none@1@S@It now only exists as an influence on the local native languages and also spoken by ⌊>Hispanic American>⌋ resident populations.@@@@1@20@@oe@26-8-2013
1000007801370@unknown@formal@none@1@S@⌊=Dialectal variation¦2=⌋@@@@1@2@@oe@26-8-2013
1000007801380@unknown@formal@none@1@S@There are important variations among the regions of Spain and throughout Spanish-speaking America.@@@@1@13@@oe@26-8-2013
1000007801390@unknown@formal@none@1@S@In countries in Hispanophone America, it is preferable to use the word ⌊/castellano/⌋ to distinguish their version of the language from that of Spain, thus asserting their autonomy and national identity.@@@@1@31@@oe@26-8-2013
1000007801400@unknown@formal@none@1@S@In Spain the Castilian dialect's pronunciation is commonly regarded as the national standard, although a use of slightly different pronouns called ⌊>⌊λ⌊/laísmo/⌋¦es¦⌊/laísmo/⌋¦Langλ⌋>⌋ of this dialect is deprecated.@@@@1@27@@oe@26-8-2013
1000007801410@unknown@formal@none@1@S@More accurately, for nearly everyone in Spain, "standard Spanish" means "pronouncing everything exactly as it is written," an ideal which does not correspond to any real dialect, though the northern dialects are the closest to it.@@@@1@36@@oe@26-8-2013
1000007801420@unknown@formal@none@1@S@In practice, the standard way of speaking Spanish in the media is "written Spanish" for formal speech, "Madrid dialect" (one of the transitional variants between Castilian and Andalusian) for informal speech.@@@@1@31@@oe@26-8-2013
1000007801430@unknown@formal@none@1@S@⌊=Voseo¦3=⌋@@@@1@1@@oe@26-8-2013
1000007801440@unknown@formal@none@1@S@Spanish has three ⌊>second-person>⌋ ⌊>singular>⌋ ⌊>pronoun>⌋s: ⌊λ⌊/tú/⌋¦es¦⌊/tú/⌋¦Langλ⌋, ⌊λ⌊/usted/⌋¦es¦⌊/usted/⌋¦Langλ⌋, and in some parts of Latin America, ⌊λ⌊/vos/⌋¦es¦⌊/vos/⌋¦Langλ⌋ (the use of this pronoun and/or its verb forms is called ⌊/voseo/⌋).@@@@1@28@@oe@26-8-2013
1000007801450@unknown@formal@none@1@S@In those regions where it is used, generally speaking, ⌊λ⌊/tú/⌋¦es¦⌊/tú/⌋¦Langλ⌋ and ⌊λ⌊/vos/⌋¦es¦⌊/vos/⌋¦Langλ⌋ are informal and used with friends; in other countries, ⌊λ⌊/vos/⌋¦es¦⌊/vos/⌋¦Langλ⌋ is considered an archaic form.@@@@1@27@@oe@26-8-2013
1000007801460@unknown@formal@none@1@S@⌊λ⌊/Usted/⌋¦es¦⌊/Usted/⌋¦Langλ⌋ is universally regarded as the formal address (derived from ⌊λ⌊/vuestra merced/⌋¦es¦⌊/vuestra merced/⌋¦Langλ⌋, "your grace"), and is used as a mark of respect, as when addressing one's elders or strangers.@@@@1@30@@oe@26-8-2013
1000007801470@unknown@formal@none@1@S@⌊λ⌊/Vos/⌋¦es¦⌊/Vos/⌋¦Langλ⌋ is used extensively as the primary spoken form of the second-person singular pronoun, although with wide differences in social consideration, in many countries of ⌊>Latin America>⌋, including ⌊>Argentina>⌋, ⌊>Chile>⌋, ⌊>Costa Rica>⌋, the central mountain region of ⌊>Ecuador>⌋, the State of ⌊>Chiapas>⌋ in ⌊>Mexico>⌋, ⌊>El Salvador>⌋, ⌊>Guatemala>⌋, ⌊>Honduras>⌋, ⌊>Nicaragua>⌋, ⌊>Paraguay>⌋, ⌊>Uruguay>⌋, the ⌊>Paisa region>⌋ and Caleños of ⌊>Colombia>⌋ and the ⌊>States>⌋ of ⌊>Zulia>⌋ and Trujillo in ⌊>Venezuela>⌋.@@@@1@67@@oe@26-8-2013
1000007801480@unknown@formal@none@1@S@There are some differences in the verbal endings for ⌊/vos/⌋ in each country.@@@@1@13@@oe@26-8-2013
1000007801490@unknown@formal@none@1@S@In Argentina, Uruguay, and increasingly in Paraguay and some Central American countries, it is also the standard form used in the ⌊>media>⌋, but the media in other countries with ⌊λ⌊/voseo/⌋¦es¦⌊/voseo/⌋¦Langλ⌋ generally continue to use ⌊λ⌊/usted/⌋¦es¦⌊/usted/⌋¦Langλ⌋ or ⌊λ⌊/tú/⌋¦es¦⌊/tú/⌋¦Langλ⌋ except in advertisements, for instance.@@@@1@42@@oe@26-8-2013
1000007801500@unknown@formal@none@1@S@⌊λ⌊/Vos/⌋¦es¦⌊/Vos/⌋¦Langλ⌋ may also be used regionally in other countries.@@@@1@9@@oe@26-8-2013
1000007801510@unknown@formal@none@1@S@Depending on country or region, usage may be considered standard or (by better educated speakers) to be unrefined.@@@@1@18@@oe@26-8-2013
1000007801520@unknown@formal@none@1@S@Interpersonal situations in which the use of ⌊/vos/⌋ is acceptable may also differ considerably between regions.@@@@1@16@@oe@26-8-2013
1000007801530@unknown@formal@none@1@S@⌊=Ustedes¦3=⌋@@@@1@1@@oe@26-8-2013
1000007801540@unknown@formal@none@1@S@Spanish forms also differ regarding second-person plural pronouns.@@@@1@8@@oe@26-8-2013
1000007801550@unknown@formal@none@1@S@The Spanish dialects of Latin America have only one form of the second-person plural for daily use, ⌊λ⌊/ustedes/⌋¦es¦⌊/ustedes/⌋¦Langλ⌋ (formal or familiar, as the case may be, though ⌊λ⌊/vosotros/⌋¦es¦⌊/vosotros/⌋¦Langλ⌋ non-formal usage can sometimes appear in poetry and rhetorical or literary style).@@@@1@40@@oe@26-8-2013
1000007801560@unknown@formal@none@1@S@In Spain there are two forms — ⌊λ⌊/ustedes/⌋¦es¦⌊/ustedes/⌋¦Langλ⌋ (formal) and ⌊λ⌊/vosotros/⌋¦es¦⌊/vosotros/⌋¦Langλ⌋ (familiar).@@@@1@12@@oe@26-8-2013
1000007801570@unknown@formal@none@1@S@The pronoun ⌊λ⌊/vosotros/⌋¦es¦⌊/vosotros/⌋¦Langλ⌋ is the plural form of ⌊λ⌊/tú/⌋¦es¦⌊/tú/⌋¦Langλ⌋ in most of Spain, but in the Americas (and certain southern Spanish cities such as ⌊>Cádiz>⌋ or ⌊>Seville>⌋, and in the ⌊>Canary Islands>⌋) it is replaced with ⌊λ⌊/ustedes/⌋¦es¦⌊/ustedes/⌋¦Langλ⌋.@@@@1@37@@oe@26-8-2013
1000007801580@unknown@formal@none@1@S@It is notable that the use of ⌊λ⌊/ustedes/⌋¦es¦⌊/ustedes/⌋¦Langλ⌋ for the informal plural "you" in southern Spain does not follow the usual rule for pronoun-verb ⌊>agreement>⌋; e.g., while the formal form for "you go", ⌊λ⌊/ustedes van/⌋¦es¦⌊/ustedes van/⌋¦Langλ⌋, uses the third-person plural form of the verb, in Cádiz or Seville the informal form is constructed as ⌊λ⌊/ustedes vais/⌋¦es¦⌊/ustedes vais/⌋¦Langλ⌋, using the second-person plural of the verb.@@@@1@64@@oe@26-8-2013
1000007801590@unknown@formal@none@1@S@In the Canary Islands, though, the usual pronoun-verb agreement is preserved in most cases.@@@@1@14@@oe@26-8-2013
1000007801600@unknown@formal@none@1@S@Some words can be different, even embarrassingly so, in different Hispanophone countries.@@@@1@12@@oe@26-8-2013
1000007801610@unknown@formal@none@1@S@Most Spanish speakers can recognize other Spanish forms, even in places where they are not commonly used, but Spaniards generally do not recognise specifically American usages.@@@@1@26@@oe@26-8-2013
1000007801620@unknown@formal@none@1@S@For example, Spanish ⌊/mantequilla/⌋, ⌊/aguacate/⌋ and ⌊/albaricoque/⌋ (respectively, "butter", "avocado", "apricot") correspond to ⌊/manteca/⌋, ⌊/palta/⌋, and ⌊/damasco/⌋, respectively, in Argentina, Chile and Uruguay.@@@@1@23@@oe@26-8-2013
1000007801630@unknown@formal@none@1@S@The everyday Spanish words ⌊/coger/⌋ (to catch, get, or pick up), ⌊/pisar/⌋ (to step on) and ⌊/concha/⌋ (seashell) are considered extremely rude in parts of Latin America, where the meaning of ⌊/coger/⌋ and ⌊/pisar/⌋ is also "to have sex" and ⌊/concha/⌋ means "vulva".@@@@1@43@@oe@26-8-2013
1000007801640@unknown@formal@none@1@S@The Puerto Rican word for "bobby pin" (⌊/pinche/⌋) is an obscenity in Mexico, and in ⌊>Nicaragua>⌋ simply means "stingy".@@@@1@19@@oe@26-8-2013
1000007801650@unknown@formal@none@1@S@Other examples include ⌊/⌊>taco>⌋/⌋, which means "swearword" in Spain but is known to the rest of the world as a Mexican dish.@@@@1@22@@oe@26-8-2013
1000007801660@unknown@formal@none@1@S@⌊/Pija/⌋ in many countries of Latin America is an obscene slang word for "penis", while in ⌊>Spain>⌋ the word also signifies "posh girl" or "snobby".@@@@1@25@@oe@26-8-2013
1000007801670@unknown@formal@none@1@S@⌊/Coche/⌋, which means "car" in Spain, for the vast majority of Spanish-speakers actually means "baby-stroller", in Guatemala it means "pig", while ⌊/carro/⌋ means "car" in some Latin American countries and "cart" in others, as well as in Spain.@@@@1@38@@oe@26-8-2013
1000007801680@unknown@formal@none@1@S@The ⌊λ⌊>Real Academia Española>⌋¦es¦⌊>Real Academia Española>⌋¦Langλ⌋ (Royal Spanish Academy), together with the 21 other national ones (see ⌊>Association of Spanish Language Academies>⌋), exercises a standardizing influence through its publication of dictionaries and widely respected grammar and style guides.@@@@1@38@@oe@26-8-2013
1000007801690@unknown@formal@none@1@S@Due to this influence and for other sociohistorical reasons, a standardized form of the language (⌊>Standard Spanish>⌋) is widely acknowledged for use in literature, academic contexts and the media.@@@@1@29@@oe@26-8-2013
1000007801700@unknown@formal@none@1@S@⌊=Writing system¦2=⌋@@@@1@2@@oe@26-8-2013
1000007801710@unknown@formal@none@1@S@Spanish is written using the ⌊>Latin alphabet>⌋, with the addition of the character ⌊/⌊>ñ>⌋/⌋ (⌊/eñe/⌋, representing the phoneme ⌊λ/ɲ/¦/ɲ/¦IPAλ⌋, a letter distinct from ⌊/n/⌋, although typographically composed of an ⌊/n/⌋ with a ⌊>tilde>⌋) and the ⌊>digraph>⌋s ⌊/ch/⌋ (⌊λ⌊/che/⌋¦es¦⌊/che/⌋¦Langλ⌋, representing the phoneme ⌊λ/tʃ/¦/tʃ/¦IPAλ⌋) and ⌊/ll/⌋ (⌊λ⌊/elle/⌋¦es¦⌊/elle/⌋¦Langλ⌋, representing the phoneme ⌊λ/ʎ/¦/ʎ/¦IPAλ⌋).@@@@1@49@@oe@26-8-2013
1000007801720@unknown@formal@none@1@S@However, the digraph ⌊/rr/⌋ (⌊λ⌊/erre fuerte/⌋¦es¦⌊/erre fuerte/⌋¦Langλ⌋, "strong ⌊/r/⌋", ⌊λ⌊/erre doble/⌋¦es¦⌊/erre doble/⌋¦Langλ⌋, "double ⌊/r/⌋", or simply ⌊λ⌊/erre/⌋¦es¦⌊/erre/⌋¦Langλ⌋), which also represents a distinct phoneme ⌊λ/r/¦/r/¦IPAλ⌋, is not similarly regarded as a single letter.@@@@1@32@@oe@26-8-2013
1000007801730@unknown@formal@none@1@S@Since 1994, the digraphs ⌊/ch/⌋ and ⌊/ll/⌋ are to be treated as letter pairs for ⌊>collation>⌋ purposes, though they remain a part of the alphabet.@@@@1@25@@oe@26-8-2013
1000007801740@unknown@formal@none@1@S@Words with ⌊/ch/⌋ are now alphabetically sorted between those with ⌊/ce/⌋ and ⌊/ci/⌋, instead of following ⌊/cz/⌋ as they used to, and similarly for ⌊/ll/⌋.@@@@1@25@@oe@26-8-2013
1000007801750@unknown@formal@none@1@S@Thus, the Spanish alphabet has the following 29 letters:@@@@1@9@@oe@26-8-2013
1000007801760@unknown@formal@none@1@S@⌊⇥a, b, c, ch, d, e, f, g, h, i, j, k, l, ll, m, n, ñ, o, p, q, r, s, t, u, v, w, x, y, z.⇥⌋@@@@1@29@@oe@26-8-2013
1000007801770@unknown@formal@none@1@S@With the exclusion of a very small number of regional terms such as ⌊/México/⌋ (see ⌊>Toponymy of Mexico>⌋) and some neologisms like ⌊/software/⌋, pronunciation can be entirely determined from spelling.@@@@1@30@@oe@26-8-2013
1000007801780@unknown@formal@none@1@S@A typical Spanish word is stressed on the ⌊>syllable>⌋ before the last if it ends with a vowel (not including ⌊/y/⌋) or with a vowel followed by ⌊/n/⌋ or ⌊/s/⌋; it is stressed on the last syllable otherwise.@@@@1@38@@oe@26-8-2013
1000007801790@unknown@formal@none@1@S@Exceptions to this rule are indicated by placing an ⌊>acute accent>⌋ on the ⌊>stressed vowel>⌋.@@@@1@15@@oe@26-8-2013
1000007801800@unknown@formal@none@1@S@The acute accent is used, in addition, to distinguish between certain ⌊>homophone>⌋s, especially when one of them is a stressed word and the other one is a ⌊>clitic>⌋: compare ⌊λ⌊/el/⌋¦es¦⌊/el/⌋¦Langλ⌋ ("the", masculine singular definite article) with ⌊λ⌊/él/⌋¦es¦⌊/él/⌋¦Langλ⌋ ("he" or "it"), or ⌊λ⌊/te/⌋¦es¦⌊/te/⌋¦Langλ⌋ ("you", object pronoun), ⌊λ⌊/de/⌋¦es¦⌊/de/⌋¦Langλ⌋ (preposition "of" or "from"), and ⌊λ⌊/se/⌋¦es¦⌊/se/⌋¦Langλ⌋ (reflexive pronoun) with ⌊λ⌊/té/⌋¦es¦⌊/té/⌋¦Langλ⌋ ("tea"), ⌊λ⌊/dé/⌋¦es¦⌊/dé/⌋¦Langλ⌋ ("give") and ⌊λ⌊/sé/⌋¦es¦⌊/sé/⌋¦Langλ⌋ ("I know", or imperative "be").@@@@1@66@@oe@26-8-2013
1000007801810@unknown@formal@none@1@S@The interrogative pronouns (⌊λ⌊/qué/⌋¦es¦⌊/qué/⌋¦Langλ⌋, ⌊λ⌊/cuál/⌋¦es¦⌊/cuál/⌋¦Langλ⌋, ⌊λ⌊/dónde/⌋¦es¦⌊/dónde/⌋¦Langλ⌋, ⌊λ⌊/quién/⌋¦es¦⌊/quién/⌋¦Langλ⌋, etc.) also receive accents in direct or indirect questions, and some demonstratives (⌊λ⌊/ése/⌋¦es¦⌊/ése/⌋¦Langλ⌋, ⌊λ⌊/éste/⌋¦es¦⌊/éste/⌋¦Langλ⌋, ⌊λ⌊/aquél/⌋¦es¦⌊/aquél/⌋¦Langλ⌋, etc.) must be accented when used as pronouns.@@@@1@30@@oe@26-8-2013
1000007801820@unknown@formal@none@1@S@The conjunction ⌊λ⌊/o/⌋¦es¦⌊/o/⌋¦Langλ⌋ ("or") is written with an accent between numerals so as not to be confused with a zero: e.g., ⌊λ⌊/10 ó 20/⌋¦es¦⌊/10 ó 20/⌋¦Langλ⌋ should be read as ⌊λ⌊/diez o veinte/⌋¦es¦⌊/diez o veinte/⌋¦Langλ⌋ rather than ⌊λ⌊/diez mil veinte/⌋¦es¦⌊/diez mil veinte/⌋¦Langλ⌋ ("10,020").@@@@1@43@@oe@26-8-2013
1000007801830@unknown@formal@none@1@S@Accent marks are frequently omitted in capital letters (a widespread practice in the early days of computers where only lowercase vowels were available with accents), although the ⌊>RAE>⌋ advises against this.@@@@1@31@@oe@26-8-2013
1000007801840@unknown@formal@none@1@S@When ⌊/u/⌋ is written between ⌊/g/⌋ and a front vowel (⌊/e/⌋ or ⌊/i/⌋), if it should be pronounced, it is written with a ⌊>diaeresis>⌋ (⌊/ü/⌋) to indicate that it is not silent as it normally would be (e.g., ⌊/cigüeña/⌋, "stork", is pronounced ⌊λ/θiˈɣweɲa/¦/θiˈɣweɲa/¦IPAλ⌋; if it were written ⌊/cigueña/⌋, it would be pronounced ⌊λ/θiˈɣeɲa/¦/θiˈɣeɲa/¦IPAλ⌋.@@@@1@53@@oe@26-8-2013
1000007801850@unknown@formal@none@1@S@Interrogative and exclamatory clauses are introduced with ⌊>inverted question ( ¿ ) and exclamation ( ¡ ) marks>⌋.@@@@1@18@@oe@26-8-2013
1000007801860@unknown@formal@none@1@S@⌊=Sounds¦2=⌋@@@@1@1@@oe@26-8-2013
1000007801870@unknown@formal@none@1@S@The phonemic inventory listed in the following table includes ⌊>phoneme>⌋s that are preserved only in some dialects, other dialects having merged them (such as ⌊/⌊>yeísmo>⌋/⌋); these are marked with an asterisk (*).@@@@1@32@@oe@26-8-2013
1000007801880@unknown@formal@none@1@S@Sounds in parentheses are ⌊>allophone>⌋s.@@@@1@5@@oe@26-8-2013
1000007801890@unknown@formal@none@1@S@By the 16th century, the consonant system of Spanish underwent the following important changes that differentiated it from ⌊>neighboring Romance languages>⌋ such as ⌊>Portuguese>⌋ and ⌊>Catalan>⌋:@@@@1@26@@oe@26-8-2013
1000007801900@unknown@formal@none@1@S@⌊•⌊#Initial ⌊λ/f/¦/f/¦IPAλ⌋, when it had evolved into a vacillating ⌊λ/h/¦/h/¦IPAλ⌋, was lost in most words (although this etymological ⌊/h-/⌋ is preserved in spelling and in some Andalusian dialects is still aspirated).#⌋@@@@1@31@@oe@26-8-2013
1000007801910@unknown@formal@none@1@S@⌊#The ⌊>bilabial approximant>⌋ ⌊λ/β̞/¦/β̞/¦IPAλ⌋ (which was written ⌊/u/⌋ or ⌊/v/⌋) merged with the bilabial oclusive ⌊λ/b/¦/b/¦IPAλ⌋ (written ⌊/b/⌋).@@@@1@18@@oe@26-8-2013
1000007801920@unknown@formal@none@1@S@There is no difference between the pronunciation of orthographic ⌊/b/⌋ and ⌊/v/⌋ in contemporary Spanish, excepting emphatic pronunciations that cannot be considered standard or natural.#⌋@@@@1@25@@oe@26-8-2013
1000007801930@unknown@formal@none@1@S@⌊#The ⌊>voiced alveolar fricative>⌋ ⌊λ/z/¦/z/¦IPAλ⌋ which existed as a separate phoneme in medieval Spanish merged with its voiceless counterpart ⌊λ/s/¦/s/¦IPAλ⌋.@@@@1@20@@oe@26-8-2013
1000007801940@unknown@formal@none@1@S@The phoneme which resulted from this merger is currently spelled ⌊/s/⌋.#⌋@@@@1@11@@oe@26-8-2013
1000007801950@unknown@formal@none@1@S@⌊#The ⌊>voiced postalveolar fricative>⌋ ⌊λ/ʒ/¦/ʒ/¦IPAλ⌋ merged with its voiceless counterpart ⌊λ/ʃ/¦/ʃ/¦IPAλ⌋, which evolved into the modern velar sound ⌊λ/x/¦/x/¦IPAλ⌋ by the 17th century, now written with ⌊/j/⌋, or ⌊/g/⌋ before ⌊/e, i/⌋.@@@@1@32@@oe@26-8-2013
1000007801960@unknown@formal@none@1@S@Nevertheless, in most parts of Argentina and in Uruguay, ⌊/y/⌋ and ⌊/ll/⌋ have both evolved to ⌊λ/ʒ/¦/ʒ/¦IPAλ⌋ or ⌊λ/ʃ/¦/ʃ/¦IPAλ⌋.#⌋@@@@1@19@@oe@26-8-2013
1000007801970@unknown@formal@none@1@S@⌊#The ⌊>voiced alveolar affricate>⌋ ⌊λ/dz/¦/dz/¦IPAλ⌋ merged with its voiceless counterpart ⌊λ/ts/¦/ts/¦IPAλ⌋, which then developed into the interdental ⌊λ/θ/¦/θ/¦IPAλ⌋, now written ⌊/z/⌋, or ⌊/c/⌋ before ⌊/e, i/⌋.@@@@1@26@@oe@26-8-2013
1000007801980@unknown@formal@none@1@S@But in ⌊>Andalusia>⌋, the ⌊>Canary Islands>⌋ and the Americas this sound merged with ⌊λ/s/¦/s/¦IPAλ⌋ as well.@@@@1@16@@oe@26-8-2013
1000007801990@unknown@formal@none@1@S@See ⌊/⌊>Ceceo>⌋/⌋, for further information.#⌋•⌋@@@@1@5@@oe@26-8-2013
1000007802000@unknown@formal@none@1@S@The consonant system of Medieval Spanish has been better preserved in ⌊>Ladino>⌋ and in Portuguese, neither of which underwent these shifts.@@@@1@21@@oe@26-8-2013
1000007802010@unknown@formal@none@1@S@⌊=Lexical stress¦3=⌋@@@@1@2@@oe@26-8-2013
1000007802020@unknown@formal@none@1@S@Spanish is a ⌊>syllable-timed language>⌋, so each syllable has the same duration regardless of stress.@@@@1@15@@oe@26-8-2013
1000007802030@unknown@formal@none@1@S@Stress most often occurs on any of the last three syllables of a word, with some rare exceptions at the fourth last.@@@@1@22@@oe@26-8-2013
1000007802040@unknown@formal@none@1@S@The ⌊/tendencies/⌋ of stress assignment are as follows:@@@@1@8@@oe@26-8-2013
1000007802050@unknown@formal@none@1@S@⌊•⌊#In words ending in vowels and ⌊λ/s/¦/s/¦IPAλ⌋, stress most often falls on the penultimate syllable.#⌋@@@@1@15@@oe@26-8-2013
1000007802060@unknown@formal@none@1@S@⌊#In words ending in all other consonants, the stress more often falls on the ultimate syllable.#⌋@@@@1@16@@oe@26-8-2013
1000007802070@unknown@formal@none@1@S@⌊#Preantepenultimate stress occurs rarely and only in words like ⌊/guardándoselos/⌋ ('saving them for him/her') where a clitic follows certain verbal forms.#⌋•⌋@@@@1@21@@oe@26-8-2013
1000007802080@unknown@formal@none@1@S@In addition to the many exceptions to these tendencies, there are numerous ⌊>minimal pair>⌋s which contrast solely on stress.@@@@1@19@@oe@26-8-2013
1000007802090@unknown@formal@none@1@S@For example, ⌊/sabana/⌋, with penultimate stress, means 'savannah' while ⌊/⌊λsábana¦es¦sábana¦Langλ⌋/⌋, with antepenultimate stress, means 'sheet'; ⌊/⌊λlímite¦es¦límite¦Langλ⌋/⌋ ('boundary'), ⌊/⌊λlimite¦es¦limite¦Langλ⌋/⌋ ('[that] he/she limits') and ⌊/⌊λlimité¦es¦limité¦Langλ⌋/⌋ ('I limited') also contrast solely on stress.@@@@1@30@@oe@26-8-2013
1000007802100@unknown@formal@none@1@S@Phonological stress may be marked orthographically with an ⌊>acute accent>⌋ (⌊/ácido/⌋, ⌊/distinción/⌋, etc).@@@@1@13@@oe@26-8-2013
1000007802110@unknown@formal@none@1@S@This is done according to the mandatory stress rules of ⌊>Spanish orthography>⌋ which are similar to the tendencies above (differing with words like ⌊/distinción/⌋) and are defined so as to unequivocally indicate where the stress lies in a given written word.@@@@1@41@@oe@26-8-2013
1000007802120@unknown@formal@none@1@S@An acute accent may also be used to differentiate homophones (such as ⌊/⌊>té>⌋/⌋ for 'tea' and ⌊/⌊>te>⌋/⌋@@@@1@17@@oe@26-8-2013
1000007802130@unknown@formal@none@1@S@An amusing example of the significance of intonation in Spanish is the phrase ⌊/⌊λ¿Cómo "cómo como"?¦es¦¿Cómo "cómo como"?¦Langλ⌋@@@@1@18@@oe@26-8-2013
1000007802140@unknown@formal@none@1@S@⌊λ¡Como como como!¦es¦¡Como como como!¦Langλ⌋/⌋@@@@1@5@@oe@26-8-2013
1000007802150@unknown@formal@none@1@S@("What do you mean / 'how / do I eat'? / I eat / the way / I eat!").@@@@1@19@@oe@26-8-2013
1000007802160@unknown@formal@none@1@S@⌊=Grammar¦2=⌋@@@@1@1@@oe@26-8-2013
1000007802170@unknown@formal@none@1@S@Spanish is a relatively ⌊>inflected>⌋ language, with a two-⌊>gender>⌋ system and about fifty ⌊>conjugated>⌋ forms per ⌊>verb>⌋, but limited inflection of ⌊>noun>⌋s, ⌊>adjective>⌋s, and ⌊>determiner>⌋s.@@@@1@25@@oe@26-8-2013
1000007802180@unknown@formal@none@1@S@(For a detailed overview of verbs, see ⌊>Spanish verbs>⌋ and ⌊>Spanish irregular verbs>⌋.)@@@@1@13@@oe@26-8-2013
1000007802190@unknown@formal@none@1@S@It is ⌊>right-branching>⌋, uses ⌊>preposition>⌋s, and usually, though not always, places ⌊>adjective>⌋s after ⌊>noun>⌋s.@@@@1@14@@oe@26-8-2013
1000007802200@unknown@formal@none@1@S@Its ⌊>syntax>⌋ is generally ⌊>Subject Verb Object>⌋, though variations are common.@@@@1@11@@oe@26-8-2013
1000007802210@unknown@formal@none@1@S@It is a ⌊>pro-drop language>⌋ (allows the deletion of pronouns when pragmatically unnecessary) and ⌊>verb-framed>⌋.@@@@1@15@@oe@26-8-2013
1000007900010@unknown@formal@none@1@S@⌊δSpeech recognitionδ⌋@@@@1@2@@oe@26-8-2013
1000007900020@unknown@formal@none@1@S@⌊∗Speech recognition∗⌋ (also known as ⌊∗automatic speech recognition∗⌋ or ⌊∗computer speech recognition∗⌋) converts spoken words to machine-readable input (for example, to keypresses, using the binary code for a string of ⌊>character>⌋ codes).@@@@1@32@@oe@26-8-2013
1000007900030@unknown@formal@none@1@S@The term ⌊>voice recognition>⌋ may also be used to refer to speech recognition, but more precisely refers to ⌊∗speaker recognition∗⌋, which attempts to identify the person speaking, as opposed to what is being said.@@@@1@34@@oe@26-8-2013
1000007900040@unknown@formal@none@1@S@Speech recognition applications include voice dialing (e.g., "Call home"), call routing (e.g., "I would like to make a collect call"), ⌊>domotic>⌋ appliance control and content-based spoken audio search (e.g., find a podcast where particular words were spoken), simple data entry (e.g., entering a credit card number), preparation of structured documents (e.g., a radiology report), speech-to-text processing (e.g., ⌊>word processor>⌋s or ⌊>email>⌋s), and in aircraft ⌊>cockpit>⌋s (usually termed ⌊>Direct Voice Input>⌋).@@@@1@70@@oe@26-8-2013
1000007900050@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000007900060@unknown@formal@none@1@S@One of the most notable domains for the commercial application of speech recognition in the United States has been health care and in particular the work of the ⌊>medical transcription>⌋ist (MT).@@@@1@31@@oe@26-8-2013
1000007900070@unknown@formal@none@1@S@According to industry experts, at its inception, speech recognition (SR) was sold as a way to completely eliminate transcription rather than make the transcription process more efficient, hence it was not accepted.@@@@1@32@@oe@26-8-2013
1000007900080@unknown@formal@none@1@S@It was also the case that SR at that time was often technically deficient.@@@@1@14@@oe@26-8-2013
1000007900090@unknown@formal@none@1@S@Additionally, to be used effectively, it required changes to the ways physicians worked and documented clinical encounters, which many if not all were reluctant to do.@@@@1@26@@oe@26-8-2013
1000007900100@unknown@formal@none@1@S@The biggest limitation to speech recognition automating transcription, however, is seen as the software.@@@@1@14@@oe@26-8-2013
1000007900110@unknown@formal@none@1@S@The nature of narrative dictation is highly interpretive and often requires judgment that may be provided by a real human but not yet by an automated system.@@@@1@27@@oe@26-8-2013
1000007900120@unknown@formal@none@1@S@Another limitation has been the extensive amount of time required by the user and/or system provider to train the software.@@@@1@20@@oe@26-8-2013
1000007900130@unknown@formal@none@1@S@A distinction in ASR is often made between "artificial syntax systems" which are usually domain-specific and "natural language processing" which is usually language-specific.@@@@1@23@@oe@26-8-2013
1000007900140@unknown@formal@none@1@S@Each of these types of application presents its own particular goals and challenges.@@@@1@13@@oe@26-8-2013
1000007900150@unknown@formal@none@1@S@⌊=Applications¦2=⌋@@@@1@1@@oe@26-8-2013
1000007900160@unknown@formal@none@1@S@⌊=Health care¦3=⌋@@@@1@2@@oe@26-8-2013
1000007900170@unknown@formal@none@1@S@In the ⌊>health care>⌋ domain, even in the wake of improving speech recognition technologies, medical transcriptionists (MTs) have not yet become obsolete.@@@@1@22@@oe@26-8-2013
1000007900180@unknown@formal@none@1@S@Many experts in the field anticipate that with increased use of speech recognition technology, the services provided may be redistributed rather than replaced.@@@@1@23@@oe@26-8-2013
1000007900190@unknown@formal@none@1@S@Speech recognition can be implemented in front-end or back-end of the medical documentation process.@@@@1@14@@oe@26-8-2013
1000007900200@unknown@formal@none@1@S@Front-End SR is where the provider dictates into a speech-recognition engine, the recognized words are displayed right after they are spoken, and the dictator is responsible for editing and signing off on the document.@@@@1@34@@oe@26-8-2013
1000007900210@unknown@formal@none@1@S@It never goes through an MT/editor.@@@@1@6@@oe@26-8-2013
1000007900220@unknown@formal@none@1@S@Back-End SR or Deferred SR is where the provider dictates into a digital dictation system, and the voice is routed through a speech-recognition machine and the recognized draft document is routed along with the original voice file to the MT/editor, who edits the draft and finalizes the report.@@@@1@48@@oe@26-8-2013
1000007900230@unknown@formal@none@1@S@Deferred SR is being widely used in the industry currently.@@@@1@10@@oe@26-8-2013
1000007900240@unknown@formal@none@1@S@Many ⌊>Electronic Medical Records>⌋ (EMR) applications can be more effective and may be performed more easily when deployed in conjunction with a speech-recognition engine.@@@@1@24@@oe@26-8-2013
1000007900250@unknown@formal@none@1@S@Searches, queries, and form filling may all be faster to perform by voice than by using a keyboard.@@@@1@18@@oe@26-8-2013
1000007900260@unknown@formal@none@1@S@⌊=Military¦3=⌋@@@@1@1@@oe@26-8-2013
1000007900270@unknown@formal@none@1@S@⌊=High-performance fighter aircraft¦4=⌋@@@@1@3@@oe@26-8-2013
1000007900280@unknown@formal@none@1@S@Substantial efforts have been devoted in the last decade to the test and evaluation of speech recognition in fighter aircraft.@@@@1@20@@oe@26-8-2013
1000007900290@unknown@formal@none@1@S@Of particular note are the U.S. program in speech recognition for the Advanced Fighter Technology Integration (AFTI)/⌊>F-16>⌋ aircraft (⌊>F-16 VISTA>⌋), the program in France on installing speech recognition systems on ⌊>Mirage>⌋ aircraft, and programs in the UK dealing with a variety of aircraft platforms.@@@@1@44@@oe@26-8-2013
1000007900300@unknown@formal@none@1@S@In these programs, speech recognizers have been operated successfully in fighter aircraft with applications including: setting radio frequencies, commanding an autopilot system, setting steer-point coordinates and weapons release parameters, and controlling flight displays.@@@@1@33@@oe@26-8-2013
1000007900310@unknown@formal@none@1@S@Generally, only very limited, constrained vocabularies have been used successfully, and a major effort has been devoted to integration of the speech recognizer with the avionics system.@@@@1@27@@oe@26-8-2013
1000007900320@unknown@formal@none@1@S@Some important conclusions from the work were as follows:@@@@1@9@@oe@26-8-2013
1000007900330@unknown@formal@none@1@S@⌊•⌊#Speech recognition has definite potential for reducing pilot workload, but this potential was not realized consistently.#⌋@@@@1@16@@oe@26-8-2013
1000007900340@unknown@formal@none@1@S@⌊#Achievement of very high recognition accuracy (95% or more) was the most critical factor for making the speech recognition system useful — with lower recognition rates, pilots would not use the system.#⌋@@@@1@31@@oe@26-8-2013
1000007900350@unknown@formal@none@1@S@⌊#More natural vocabulary and grammar, and shorter training times would be useful, but only if very high recognition rates could be maintained.#⌋•⌋@@@@1@22@@oe@26-8-2013
1000007900360@unknown@formal@none@1@S@Laboratory research in robust speech recognition for military environments has produced promising results which, if extendable to the cockpit, should improve the utility of speech recognition in high-performance aircraft.@@@@1@29@@oe@26-8-2013
1000007900370@unknown@formal@none@1@S@Working with Swedish pilots flying in the ⌊>JAS-39>⌋ Gripen cockpit, Englund (2004) found recognition deteriorated with increasing G-loads.@@@@1@18@@oe@26-8-2013
1000007900380@unknown@formal@none@1@S@It was also concluded that adaptation greatly improved the results in all cases and introducing models for breathing was shown to improve recognition scores significantly.@@@@1@25@@oe@26-8-2013
1000007900390@unknown@formal@none@1@S@Contrary to what might be expected, no effects of the broken English of the speakers were found.@@@@1@17@@oe@26-8-2013
1000007900400@unknown@formal@none@1@S@It was evident that spontaneous speech caused problems for the recognizer, as could be expected.@@@@1@15@@oe@26-8-2013
1000007900410@unknown@formal@none@1@S@A restricted vocabulary, and above all, a proper syntax, could thus be expected to improve recognition accuracy substantially.@@@@1@18@@oe@26-8-2013
1000007900420@unknown@formal@none@1@S@The ⌊>Eurofighter Typhoon>⌋ currently in service with the UK ⌊>RAF>⌋ employs a speaker-dependent system, i.e. it requires each pilot to create a template.@@@@1@23@@oe@26-8-2013
1000007900430@unknown@formal@none@1@S@The system is not used for any safety critical or weapon critical tasks, such as weapon release or lowering of the undercarriage, but is used for a wide range of other ⌊>cockpit>⌋ functions.@@@@1@33@@oe@26-8-2013
1000007900440@unknown@formal@none@1@S@Voice commands are confirmed by visual and/or aural feedback.@@@@1@9@@oe@26-8-2013
1000007900450@unknown@formal@none@1@S@The system is seen as a major design feature in the reduction of pilot ⌊>workload>⌋, and even allows the pilot to assign targets to himself with two simple voice commands or to any of his wingmen with only five commands.@@@@1@40@@oe@26-8-2013
1000007900460@unknown@formal@none@1@S@⌊=Helicopters¦4=⌋@@@@1@1@@oe@26-8-2013
1000007900470@unknown@formal@none@1@S@The problems of achieving high recognition accuracy under stress and noise pertain strongly to the helicopter environment as well as to the fighter environment.@@@@1@24@@oe@26-8-2013
1000007900480@unknown@formal@none@1@S@The acoustic noise problem is actually more severe in the helicopter environment, not only because of the high noise levels but also because the helicopter pilot generally does not wear a facemask, which would reduce acoustic noise in the microphone.@@@@1@40@@oe@26-8-2013
1000007900490@unknown@formal@none@1@S@Substantial test and evaluation programs have been carried out in the post decade in speech recognition systems applications in helicopters, notably by the U.S. Army Avionics Research and Development Activity (AVRADA) and by the Royal Aerospace Establishment (RAE) in the UK.@@@@1@41@@oe@26-8-2013
1000007900500@unknown@formal@none@1@S@Work in France has included speech recognition in the Puma helicopter.@@@@1@11@@oe@26-8-2013
1000007900510@unknown@formal@none@1@S@There has also been much useful work in Canada.@@@@1@9@@oe@26-8-2013
1000007900520@unknown@formal@none@1@S@Results have been encouraging, and voice applications have included: control of communication radios; setting of navigation systems; and control of an automated target handover system.@@@@1@25@@oe@26-8-2013
1000007900530@unknown@formal@none@1@S@As in fighter applications, the overriding issue for voice in helicopters is the impact on pilot effectiveness.@@@@1@17@@oe@26-8-2013
1000007900540@unknown@formal@none@1@S@Encouraging results are reported for the AVRADA tests, although these represent only a feasibility demonstration in a test environment.@@@@1@19@@oe@26-8-2013
1000007900550@unknown@formal@none@1@S@Much remains to be done both in speech recognition and in overall speech recognition technology, in order to consistently achieve performance improvements in operational settings.@@@@1@25@@oe@26-8-2013
1000007900560@unknown@formal@none@1@S@⌊=Battle management¦4=⌋@@@@1@2@@oe@26-8-2013
1000007900570@unknown@formal@none@1@S@Battle management command centres generally require rapid access to and control of large, rapidly changing information databases.@@@@1@17@@oe@26-8-2013
1000007900580@unknown@formal@none@1@S@Commanders and system operators need to query these databases as conveniently as possible, in an eyes-busy environment where much of the information is presented in a display format.@@@@1@28@@oe@26-8-2013
1000007900590@unknown@formal@none@1@S@Human machine interaction by voice has the potential to be very useful in these environments.@@@@1@15@@oe@26-8-2013
1000007900600@unknown@formal@none@1@S@A number of efforts have been undertaken to interface commercially available isolated-word recognizers into battle management environments.@@@@1@17@@oe@26-8-2013
1000007900610@unknown@formal@none@1@S@In one feasibility study, speech recognition equipment was tested in conjunction with an integrated information display for naval battle management applications.@@@@1@21@@oe@26-8-2013
1000007900620@unknown@formal@none@1@S@Users were very optimistic about the potential of the system, although capabilities were limited.@@@@1@14@@oe@26-8-2013
1000007900630@unknown@formal@none@1@S@Speech understanding programs sponsored by the Defense Advanced Research Projects Agency (DARPA) in the U.S. has focused on this problem of natural speech interface..@@@@1@24@@oe@26-8-2013
1000007900640@unknown@formal@none@1@S@Speech recognition efforts have focused on a database of continuous speech recognition (CSR), large-vocabulary speech which is designed to be representative of the naval resource management task.@@@@1@27@@oe@26-8-2013
1000007900650@unknown@formal@none@1@S@Significant advances in the state-of-the-art in CSR have been achieved, and current efforts are focused on integrating speech recognition and natural language processing to allow spoken language interaction with a naval resource management system.@@@@1@34@@oe@26-8-2013
1000007900660@unknown@formal@none@1@S@⌊=Training air traffic controllers¦4=⌋@@@@1@4@@oe@26-8-2013
1000007900670@unknown@formal@none@1@S@Training for military (or civilian) air traffic controllers (ATC) represents an excellent application for speech recognition systems.@@@@1@17@@oe@26-8-2013
1000007900680@unknown@formal@none@1@S@Many ATC training systems currently require a person to act as a "pseudo-pilot", engaging in a voice dialog with the trainee controller, which simulates the dialog which the controller would have to conduct with pilots in a real ATC situation.@@@@1@40@@oe@26-8-2013
1000007900690@unknown@formal@none@1@S@Speech recognition and synthesis techniques offer the potential to eliminate the need for a person to act as pseudo-pilot, thus reducing training and support personnel.@@@@1@25@@oe@26-8-2013
1000007900700@unknown@formal@none@1@S@Air controller tasks are also characterized by highly structured speech as the primary output of the controller, hence reducing the difficulty of the speech recognition task.@@@@1@26@@oe@26-8-2013
1000007900710@unknown@formal@none@1@S@The U.S. Naval Training Equipment Center has sponsored a number of developments of prototype ATC trainers using speech recognition.@@@@1@19@@oe@26-8-2013
1000007900720@unknown@formal@none@1@S@Generally, the recognition accuracy falls short of providing graceful interaction between the trainee and the system.@@@@1@16@@oe@26-8-2013
1000007900730@unknown@formal@none@1@S@However, the prototype training systems have demonstrated a significant potential for voice interaction in these systems, and in other training applications.@@@@1@21@@oe@26-8-2013
1000007900740@unknown@formal@none@1@S@The U.S. Navy has sponsored a large-scale effort in ATC training systems, where a commercial speech recognition unit was integrated with a complex training system including displays and scenario creation.@@@@1@30@@oe@26-8-2013
1000007900750@unknown@formal@none@1@S@Although the recognizer was constrained in vocabulary, one of the goals of the training programs was to teach the controllers to speak in a constrained language, using specific vocabulary specifically designed for the ATC task.@@@@1@35@@oe@26-8-2013
1000007900760@unknown@formal@none@1@S@Research in France has focussed on the application of speech recognition in ATC training systems, directed at issues both in speech recognition and in application of task-domain grammar constraints.@@@@1@29@@oe@26-8-2013
1000007900770@unknown@formal@none@1@S@The USAF, USMC, US Army, and FAA are currently using ATC simulators with speech recognition provided by Adacel Systems Inc (ASI).@@@@1@21@@oe@26-8-2013
1000007900780@unknown@formal@none@1@S@Adacel's MaxSim software uses speech recognition and synthetic speech to enable the trainee to control aircraft and ground vehicles in the simulation without the need for pseudo pilots.@@@@1@28@@oe@26-8-2013
1000007900790@unknown@formal@none@1@S@Adacel's ATC In A Box Software provideds a synthetic ATC environment for flight simulators.@@@@1@14@@oe@26-8-2013
1000007900800@unknown@formal@none@1@S@The "real" pilot talks to a virtual controller using speech recognition and the virtual controller responds with synthetic speech.@@@@1@19@@oe@26-8-2013
1000007900810@unknown@formal@none@1@S@It will be an application format@@@@1@6@@oe@26-8-2013
1000007900820@unknown@formal@none@1@S@⌊=Telephony and other domains¦3=⌋@@@@1@4@@oe@26-8-2013
1000007900830@unknown@formal@none@1@S@ASR in the field of telephony is now commonplace and in the field of computer gaming and simulation is becoming more widespread.@@@@1@22@@oe@26-8-2013
1000007900840@unknown@formal@none@1@S@Despite the high level of integration with word processing in general personal computing, however, ASR in the field of document production has not seen the expected increases in use.@@@@1@29@@oe@26-8-2013
1000007900850@unknown@formal@none@1@S@The improvement of mobile processor speeds let create speech-enabled Symbian and Windows Mobile Smartphones.@@@@1@14@@oe@26-8-2013
1000007900860@unknown@formal@none@1@S@Current speech-to-text programs are too large and require too much CPU power to be practical for the Pocket PC.@@@@1@19@@oe@26-8-2013
1000007900870@unknown@formal@none@1@S@Speech is used mostly as a part of User Interface, for creating pre-defined or custom speech commands.@@@@1@17@@oe@26-8-2013
1000007900880@unknown@formal@none@1@S@Leading software vendors in this field are: Microsoft Corporation (Microsoft Voice Command); Nuance Communications (Nuance Voice Control); Vito Technology (VITO Voice2Go); Speereo Software (Speereo Voice Translator).@@@@1@26@@oe@26-8-2013
1000007900890@unknown@formal@none@1@S@⌊=People with Disabilities¦3=⌋@@@@1@3@@oe@26-8-2013
1000007900900@unknown@formal@none@1@S@People with disabilities are another part of the population that benefit from using speech recognition programs.@@@@1@16@@oe@26-8-2013
1000007900910@unknown@formal@none@1@S@It is especially useful for people who have difficulty with or are unable to use their hands, from mild repetitive stress injuries to involved disabilities that require alternative input for support with accessing the computer.@@@@1@35@@oe@26-8-2013
1000007900920@unknown@formal@none@1@S@In fact, people who used the keyboard a lot and developed ⌊>RSI>⌋ became an urgent early market for speech recognition.@@@@1@20@@oe@26-8-2013
1000007900930@unknown@formal@none@1@S@Speech recognition is used in ⌊>deaf>⌋ ⌊>telephony>⌋, such as ⌊>spinvox>⌋ voice-to-text voicemail, ⌊>relay services>⌋, and ⌊>captioned telephone>⌋.@@@@1@17@@oe@26-8-2013
1000007900940@unknown@formal@none@1@S@⌊=Further applications¦3=⌋@@@@1@2@@oe@26-8-2013
1000007900950@unknown@formal@none@1@S@⌊•⌊#Automatic translation#⌋@@@@1@2@@oe@26-8-2013
1000007900960@unknown@formal@none@1@S@⌊#Automotive speech recognition (e.g., ⌊>Ford Sync>⌋)#⌋@@@@1@6@@oe@26-8-2013
1000007900970@unknown@formal@none@1@S@⌊#Telematics (e.g. vehicle Navigation Systems)#⌋@@@@1@5@@oe@26-8-2013
1000007900980@unknown@formal@none@1@S@⌊#Court reporting (Realtime Voice Writing)#⌋@@@@1@5@@oe@26-8-2013
1000007900990@unknown@formal@none@1@S@⌊#⌊>Hands-free computing>⌋: voice command recognition computer ⌊>user interface>⌋#⌋@@@@1@8@@oe@26-8-2013
1000007901000@unknown@formal@none@1@S@⌊#⌊>Home automation>⌋@@@@1@2@@oe@26-8-2013
1000007901010@unknown@formal@none@1@S@⌊>Interactive voice response>⌋#⌋@@@@1@3@@oe@26-8-2013
1000007901020@unknown@formal@none@1@S@⌊#⌊>Mobile telephony>⌋, including mobile email#⌋@@@@1@5@@oe@26-8-2013
1000007901030@unknown@formal@none@1@S@⌊#⌊>Multimodal interaction>⌋#⌋@@@@1@2@@oe@26-8-2013
1000007901040@unknown@formal@none@1@S@⌊#⌊>Pronunciation>⌋ evaluation in computer-aided language learning applications#⌋@@@@1@7@@oe@26-8-2013
1000007901050@unknown@formal@none@1@S@⌊#⌊>Robotics>⌋#⌋@@@@1@1@@oe@26-8-2013
1000007901060@unknown@formal@none@1@S@⌊#⌊>Transcription>⌋ (digital speech-to-text).#⌋@@@@1@3@@oe@26-8-2013
1000007901070@unknown@formal@none@1@S@⌊#Speech-to-Text (Transcription of speech into mobile text messages)#⌋•⌋@@@@1@8@@oe@26-8-2013
1000007901080@unknown@formal@none@1@S@⌊=Performance of speech recognition systems¦2=⌋@@@@1@5@@oe@26-8-2013
1000007901090@unknown@formal@none@1@S@The performance of speech recognition systems is usually specified in terms of accuracy and speed.@@@@1@15@@oe@26-8-2013
1000007901100@unknown@formal@none@1@S@Accuracy may be measured in terms of performance accuracy which is usually rated with ⌊>word error rate>⌋ (WER), whereas speed is measured with the ⌊>real time factor>⌋.@@@@1@27@@oe@26-8-2013
1000007901110@unknown@formal@none@1@S@Other measures of accuracy include ⌊>Single Word Error Rate>⌋ (SWER) and ⌊>Command Success Rate>⌋ (CSR).@@@@1@15@@oe@26-8-2013
1000007901120@unknown@formal@none@1@S@Most speech recognition users would tend to agree that dictation machines can achieve very high performance in controlled conditions.@@@@1@19@@oe@26-8-2013
1000007901130@unknown@formal@none@1@S@There is some confusion, however, over the interchangeability of the terms "speech recognition" and "dictation".@@@@1@15@@oe@26-8-2013
1000007901140@unknown@formal@none@1@S@Commercially available speaker-dependent dictation systems usually require only a short period of training (sometimes also called `enrollment') and may successfully capture continuous speech with a large vocabulary at normal pace with a very high accuracy.@@@@1@35@@oe@26-8-2013
1000007901150@unknown@formal@none@1@S@Most commercial companies claim that recognition software can achieve between 98% to 99% accuracy if operated under optimal conditions.@@@@1@19@@oe@26-8-2013
1000007901160@unknown@formal@none@1@S@`Optimal conditions' usually assume that users:@@@@1@6@@oe@26-8-2013
1000007901170@unknown@formal@none@1@S@⌊•⌊#have speech characteristics which match the training data,#⌋@@@@1@8@@oe@26-8-2013
1000007901180@unknown@formal@none@1@S@⌊#can achieve proper speaker adaptation, and#⌋@@@@1@6@@oe@26-8-2013
1000007901190@unknown@formal@none@1@S@⌊#work in a clean noise environment (e.g. quiet office or laboratory space).#⌋•⌋@@@@1@12@@oe@26-8-2013
1000007901200@unknown@formal@none@1@S@This explains why some users, especially those whose speech is heavily accented, might achieve recognition rates much lower than expected.@@@@1@20@@oe@26-8-2013
1000007901210@unknown@formal@none@1@S@Speech recognition in video has become a popular search technology used by several video search companies.@@@@1@16@@oe@26-8-2013
1000007901220@unknown@formal@none@1@S@Limited vocabulary systems, requiring no training, can recognize a small number of words (for instance, the ten digits) as spoken by most speakers.@@@@1@23@@oe@26-8-2013
1000007901230@unknown@formal@none@1@S@Such systems are popular for routing incoming phone calls to their destinations in large organizations.@@@@1@15@@oe@26-8-2013
1000007901240@unknown@formal@none@1@S@Both ⌊>acoustic modeling>⌋ and ⌊>language model>⌋ing are important parts of modern statistically-based speech recognition algorithms.@@@@1@15@@oe@26-8-2013
1000007901250@unknown@formal@none@1@S@Hidden Markov models (HMMs) are widely used in many systems.@@@@1@10@@oe@26-8-2013
1000007901260@unknown@formal@none@1@S@Language modeling has many other applications such as ⌊>smart keyboard>⌋ and ⌊>document classification>⌋.@@@@1@13@@oe@26-8-2013
1000007901270@unknown@formal@none@1@S@⌊=Hidden Markov model (HMM)-based speech recognition¦3=⌋@@@@1@6@@oe@26-8-2013
1000007901280@unknown@formal@none@1@S@Modern general-purpose speech recognition systems are generally based on ⌊>HMMs>⌋.@@@@1@10@@oe@26-8-2013
1000007901290@unknown@formal@none@1@S@These are statistical models which output a sequence of symbols or quantities.@@@@1@12@@oe@26-8-2013
1000007901300@unknown@formal@none@1@S@One possible reason why HMMs are used in speech recognition is that a speech signal could be viewed as a piecewise stationary signal or a short-time stationary signal.@@@@1@28@@oe@26-8-2013
1000007901310@unknown@formal@none@1@S@That is, one could assume in a short-time in the range of 10 milliseconds, speech could be approximated as a ⌊>stationary process>⌋.@@@@1@22@@oe@26-8-2013
1000007901320@unknown@formal@none@1@S@Speech could thus be thought of as a ⌊>Markov model>⌋ for many stochastic processes.@@@@1@14@@oe@26-8-2013
1000007901330@unknown@formal@none@1@S@Another reason why HMMs are popular is because they can be trained automatically and are simple and computationally feasible to use.@@@@1@21@@oe@26-8-2013
1000007901340@unknown@formal@none@1@S@In speech recognition, the hidden Markov model would output a sequence of ⌊/n/⌋-dimensional real-valued vectors (with ⌊/n/⌋ being a small integer, such as 10), outputting one of these every 10 milliseconds.@@@@1@31@@oe@26-8-2013
1000007901350@unknown@formal@none@1@S@The vectors would consist of ⌊>cepstral>⌋ coefficients, which are obtained by taking a ⌊>Fourier transform>⌋ of a short time window of speech and decorrelating the spectrum using a ⌊>cosine transform>⌋, then taking the first (most significant) coefficients.@@@@1@37@@oe@26-8-2013
1000007901360@unknown@formal@none@1@S@The hidden Markov model will tend to have in each state a statistical distribution that is a mixture of diagonal covariance Gaussians which will give a likelihood for each observed vector.@@@@1@31@@oe@26-8-2013
1000007901370@unknown@formal@none@1@S@Each word, or (for more general speech recognition systems), each ⌊>phoneme>⌋, will have a different output distribution; a hidden Markov model for a sequence of words or phonemes is made by concatenating the individual trained hidden Markov models for the separate words and phonemes.@@@@1@44@@oe@26-8-2013
1000007901380@unknown@formal@none@1@S@Described above are the core elements of the most common, HMM-based approach to speech recognition.@@@@1@15@@oe@26-8-2013
1000007901390@unknown@formal@none@1@S@Modern speech recognition systems use various combinations of a number of standard techniques in order to improve results over the basic approach described above.@@@@1@24@@oe@26-8-2013
1000007901400@unknown@formal@none@1@S@A typical large-vocabulary system would need context dependency for the phonemes (so phonemes with different left and right context have different realizations as HMM states); it would use cepstral normalization to normalize for different speaker and recording conditions; for further speaker normalization it might use vocal tract length normalization (VTLN) for male-female normalization and maximum likelihood linear regression (MLLR) for more general speaker adaptation.@@@@1@64@@oe@26-8-2013
1000007901410@unknown@formal@none@1@S@The features would have so-called delta and delta-delta coefficients to capture speech dynamics and in addition might use heteroscedastic linear discriminant analysis (HLDA); or might skip the delta and delta-delta coefficients and use splicing and an LDA-based projection followed perhaps by heteroscedastic linear discriminant analysis or a global semitied covariance transform (also known as maximum likelihood linear transform, or MLLT).@@@@1@60@@oe@26-8-2013
1000007901420@unknown@formal@none@1@S@Many systems use so-called discriminative training techniques which dispense with a purely statistical approach to HMM parameter estimation and instead optimize some classification-related measure of the training data.@@@@1@28@@oe@26-8-2013
1000007901430@unknown@formal@none@1@S@Examples are maximum ⌊>mutual information>⌋ (MMI), minimum classification error (MCE) and minimum phone error (MPE).@@@@1@15@@oe@26-8-2013
1000007901440@unknown@formal@none@1@S@Decoding of the speech (the term for what happens when the system is presented with a new utterance and must compute the most likely source sentence) would probably use the ⌊>Viterbi algorithm>⌋ to find the best path, and here there is a choice between dynamically creating a combination hidden Markov model which includes both the acoustic and language model information, or combining it statically beforehand (the ⌊>finite state transducer>⌋, or FST, approach).@@@@1@72@@oe@26-8-2013
1000007901450@unknown@formal@none@1@S@⌊=Dynamic time warping (DTW)-based speech recognition¦3=⌋@@@@1@6@@oe@26-8-2013
1000007901460@unknown@formal@none@1@S@Dynamic time warping is an approach that was historically used for speech recognition but has now largely been displaced by the more successful HMM-based approach.@@@@1@25@@oe@26-8-2013
1000007901470@unknown@formal@none@1@S@Dynamic time warping is an algorithm for measuring similarity between two sequences which may vary in time or speed.@@@@1@19@@oe@26-8-2013
1000007901480@unknown@formal@none@1@S@For instance, similarities in walking patterns would be detected, even if in one video the person was walking slowly and if in another they were walking more quickly, or even if there were accelerations and decelerations during the course of one observation.@@@@1@42@@oe@26-8-2013
1000007901490@unknown@formal@none@1@S@DTW has been applied to video, audio, and graphics – indeed, any data which can be turned into a linear representation can be analyzed with DTW.@@@@1@25@@oe@26-8-2013
1000007901500@unknown@formal@none@1@S@A well known application has been automatic speech recognition, to cope with different speaking speeds.@@@@1@15@@oe@26-8-2013
1000007901510@unknown@formal@none@1@S@In general, it is a method that allows a computer to find an optimal match between two given sequences (e.g. time series) with certain restrictions, i.e. the sequences are "warped" non-linearly to match each other.@@@@1@35@@oe@26-8-2013
1000007901520@unknown@formal@none@1@S@This sequence alignment method is often used in the context of hidden Markov models.@@@@1@14@@oe@26-8-2013
1000007901530@unknown@formal@none@1@S@⌊=Further information¦2=⌋@@@@1@2@@oe@26-8-2013
1000007901540@unknown@formal@none@1@S@Popular speech recognition conferences held each year or two include ICASSP, Eurospeech/ICSLP (now named Interspeech) and the IEEE ASRU.@@@@1@19@@oe@26-8-2013
1000007901550@unknown@formal@none@1@S@Conferences in the field of ⌊>Natural Language Processing>⌋, such as ACL, NAACL, EMNLP, and HLT, are beginning to include papers on speech processing.@@@@1@23@@oe@26-8-2013
1000007901560@unknown@formal@none@1@S@Important journals include the ⌊>IEEE>⌋ Transactions on Speech and Audio Processing (now named ⌊>IEEE>⌋ Transactions on Audio, Speech and Language Processing), Computer Speech and Language, and Speech Communication.@@@@1@28@@oe@26-8-2013
1000007901570@unknown@formal@none@1@S@Books like "Fundamentals of Speech Recognition" by ⌊>Lawrence Rabiner>⌋ can be useful to acquire basic knowledge but may not be fully up to date (1993).@@@@1@25@@oe@26-8-2013
1000007901580@unknown@formal@none@1@S@Another good source can be "Statistical Methods for Speech Recognition" by Frederick Jelinek which is a more up to date book (1998).@@@@1@22@@oe@26-8-2013
1000007901590@unknown@formal@none@1@S@Even more up to date is "Computer Speech", by Manfred R. Schroeder, second edition published in 2004.@@@@1@17@@oe@26-8-2013
1000007901600@unknown@formal@none@1@S@A good insight into the techniques used in the best modern systems can be gained by paying attention to government sponsored evaluations such as those organised by ⌊>DARPA>⌋ (the largest speech recognition-related project ongoing as of 2007 is the GALE project, which involves both speech recognition and translation components).@@@@1@49@@oe@26-8-2013
1000007901610@unknown@formal@none@1@S@In terms of freely available resources, the ⌊>HTK>⌋ book (and the accompanying HTK toolkit) is one place to start to both learn about speech recognition and to start experimenting.@@@@1@29@@oe@26-8-2013
1000007901620@unknown@formal@none@1@S@Another such resource is ⌊>Carnegie Mellon University>⌋'s SPHINX toolkit.@@@@1@9@@oe@26-8-2013
1000007901630@unknown@formal@none@1@S@The AT&T libraries ⌊> FSM Library>⌋, ⌊> GRM library>⌋, and ⌊>DCD library>⌋ are also general software libraries for large-vocabulary speech recognition.@@@@1@21@@oe@26-8-2013
1000007901640@unknown@formal@none@1@S@A useful review of the area of robustness in ASR is provided by Junqua and Haton (1995).@@@@1@17@@oe@26-8-2013
1000008000010@unknown@formal@none@1@S@⌊δSpeech synthesisδ⌋@@@@1@2@@oe@26-8-2013
1000008000020@unknown@formal@none@1@S@⌊∗Speech synthesis∗⌋ is the artificial production of human ⌊>speech>⌋.@@@@1@9@@oe@26-8-2013
1000008000030@unknown@formal@none@1@S@A computer system used for this purpose is called a ⌊∗speech synthesizer∗⌋, and can be implemented in ⌊>software>⌋ or ⌊>hardware>⌋.@@@@1@20@@oe@26-8-2013
1000008000040@unknown@formal@none@1@S@A ⌊∗text-to-speech (TTS)∗⌋ system converts normal language text into speech; other systems render ⌊>symbolic linguistic representation>⌋s like ⌊>phonetic transcription>⌋s into speech.@@@@1@21@@oe@26-8-2013
1000008000050@unknown@formal@none@1@S@Synthesized speech can be created by concatenating pieces of recorded speech that are stored in a ⌊>database>⌋.@@@@1@17@@oe@26-8-2013
1000008000060@unknown@formal@none@1@S@Systems differ in the size of the stored speech units; a system that stores ⌊>phone>⌋s or ⌊>diphone>⌋s provides the largest output range, but may lack clarity.@@@@1@26@@oe@26-8-2013
1000008000070@unknown@formal@none@1@S@For specific usage domains, the storage of entire words or sentences allows for high-quality output.@@@@1@15@@oe@26-8-2013
1000008000080@unknown@formal@none@1@S@Alternatively, a synthesizer can incorporate a model of the ⌊>vocal tract>⌋ and other human voice characteristics to create a completely "synthetic" voice output.@@@@1@23@@oe@26-8-2013
1000008000090@unknown@formal@none@1@S@The quality of a speech synthesizer is judged by its similarity to the human voice, and by its ability to be understood.@@@@1@22@@oe@26-8-2013
1000008000100@unknown@formal@none@1@S@An intelligible text-to-speech program allows people with ⌊>visual impairment>⌋s or ⌊>reading disabilities>⌋ to listen to written works on a home computer.@@@@1@21@@oe@26-8-2013
1000008000110@unknown@formal@none@1@S@Many computer operating systems have included speech synthesizers since the early 1980s.@@@@1@12@@oe@26-8-2013
1000008000120@unknown@formal@none@1@S@⌊=Overview of text processing¦2=⌋@@@@1@4@@oe@26-8-2013
1000008000130@unknown@formal@none@1@S@A text-to-speech system (or "engine") is composed of two parts: a ⌊>front-end>⌋ and a back-end.@@@@1@15@@oe@26-8-2013
1000008000140@unknown@formal@none@1@S@The front-end has two major tasks.@@@@1@6@@oe@26-8-2013
1000008000150@unknown@formal@none@1@S@First, it converts raw text containing symbols like numbers and abbreviations into the equivalent of written-out words.@@@@1@17@@oe@26-8-2013
1000008000160@unknown@formal@none@1@S@This process is often called ⌊/text normalization/⌋, ⌊/pre-processing/⌋, or ⌊/⌊>tokenization>⌋/⌋.@@@@1@10@@oe@26-8-2013
1000008000170@unknown@formal@none@1@S@The front-end then assigns ⌊>phonetic transcription>⌋s to each word, and divides and marks the text into ⌊>prosodic units>⌋, like ⌊>phrase>⌋s, ⌊>clause>⌋s, and ⌊>sentence>⌋s.@@@@1@23@@oe@26-8-2013
1000008000180@unknown@formal@none@1@S@The process of assigning phonetic transcriptions to words is called ⌊/text-to-phoneme/⌋ or ⌊/⌊>grapheme>⌋-to-phoneme/⌋ conversion.@@@@1@14@@oe@26-8-2013
1000008000190@unknown@formal@none@1@S@Phonetic transcriptions and prosody information together make up the symbolic linguistic representation that is output by the front-end.@@@@1@18@@oe@26-8-2013
1000008000200@unknown@formal@none@1@S@The back-end—often referred to as the ⌊/synthesizer/⌋—then converts the symbolic linguistic representation into sound.@@@@1@14@@oe@26-8-2013
1000008000210@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000008000220@unknown@formal@none@1@S@Long before ⌊>electronic>⌋ ⌊>signal processing>⌋ was invented, there were those who tried to build machines to create human speech.@@@@1@19@@oe@26-8-2013
1000008000230@unknown@formal@none@1@S@Some early legends of the existence of ⌊>"speaking heads">⌋ involved ⌊>Gerbert of Aurillac>⌋ (d. 1003 AD), ⌊>Albertus Magnus>⌋ (1198–1280), and ⌊>Roger Bacon>⌋ (1214–1294).@@@@1@23@@oe@26-8-2013
1000008000240@unknown@formal@none@1@S@In 1779, the ⌊>Danish>⌋ scientist Christian Kratzenstein, working at the ⌊>Russian Academy of Sciences>⌋, built models of the human ⌊>vocal tract>⌋ that could produce the five long ⌊>vowel>⌋ sounds (in ⌊>International Phonetic Alphabet>⌋ notation, they are ⌊λ[aː]¦[aː]¦IPAλ⌋, ⌊λ[eː]¦[eː]¦IPAλ⌋, ⌊λ[iː]¦[iː]¦IPAλ⌋, ⌊λ[oː]¦[oː]¦IPAλ⌋ and ⌊λ[uː]¦[uː]¦IPAλ⌋).@@@@1@42@@oe@26-8-2013
1000008000250@unknown@formal@none@1@S@This was followed by the ⌊>bellows>⌋-operated "acoustic-mechanical speech machine" by ⌊>Wolfgang von Kempelen>⌋ of ⌊>Vienna>⌋, ⌊>Austria>⌋, described in a 1791 paper.@@@@1@21@@oe@26-8-2013
1000008000260@unknown@formal@none@1@S@This machine added models of the tongue and lips, enabling it to produce ⌊>consonant>⌋s as well as vowels.@@@@1@18@@oe@26-8-2013
1000008000270@unknown@formal@none@1@S@In 1837, ⌊>Charles Wheatstone>⌋ produced a "speaking machine" based on von Kempelen's design, and in 1857, M. Faber built the "Euphonia".@@@@1@21@@oe@26-8-2013
1000008000280@unknown@formal@none@1@S@Wheatstone's design was resurrected in 1923 by Paget.@@@@1@8@@oe@26-8-2013
1000008000290@unknown@formal@none@1@S@In the 1930s, ⌊>Bell Labs>⌋ developed the ⌊>VOCODER>⌋, a keyboard-operated electronic speech analyzer and synthesizer that was said to be clearly intelligible.@@@@1@22@@oe@26-8-2013
1000008000300@unknown@formal@none@1@S@⌊>Homer Dudley>⌋ refined this device into the VODER, which he exhibited at the ⌊>1939 New York World's Fair>⌋.@@@@1@18@@oe@26-8-2013
1000008000310@unknown@formal@none@1@S@The ⌊>Pattern playback>⌋ was built by ⌊>Dr. Franklin S. Cooper>⌋ and his colleagues at ⌊>Haskins Laboratories>⌋ in the late 1940s and completed in 1950.@@@@1@24@@oe@26-8-2013
1000008000320@unknown@formal@none@1@S@There were several different versions of this hardware device but only one currently survives.@@@@1@14@@oe@26-8-2013
1000008000330@unknown@formal@none@1@S@The machine converts pictures of the acoustic patterns of speech in the form of a spectrogram back into sound.@@@@1@19@@oe@26-8-2013
1000008000340@unknown@formal@none@1@S@Using this device, ⌊>Alvin Liberman>⌋ and colleagues were able to discover acoustic cues for the perception of ⌊>phonetic>⌋ segments (consonants and vowels).@@@@1@22@@oe@26-8-2013
1000008000350@unknown@formal@none@1@S@Early electronic speech synthesizers sounded robotic and were often barely intelligible.@@@@1@11@@oe@26-8-2013
1000008000360@unknown@formal@none@1@S@However, the quality of synthesized speech has steadily improved, and output from contemporary speech synthesis systems is sometimes indistinguishable from actual human speech.@@@@1@23@@oe@26-8-2013
1000008000370@unknown@formal@none@1@S@⌊=Electronic devices¦3=⌋@@@@1@2@@oe@26-8-2013
1000008000380@unknown@formal@none@1@S@The first computer-based speech synthesis systems were created in the late 1950s, and the first complete text-to-speech system was completed in 1968.@@@@1@22@@oe@26-8-2013
1000008000390@unknown@formal@none@1@S@In 1961, physicist ⌊>John Larry Kelly, Jr>⌋ and colleague Louis Gerstman used an ⌊>IBM 704>⌋ computer to synthesize speech, an event among the most prominent in the history of ⌊>Bell Labs>⌋.@@@@1@31@@oe@26-8-2013
1000008000400@unknown@formal@none@1@S@Kelly's voice recorder synthesizer (vocoder) recreated the song "⌊>Daisy Bell>⌋", with musical accompaniment from ⌊>Max Mathews>⌋.@@@@1@16@@oe@26-8-2013
1000008000410@unknown@formal@none@1@S@Coincidentally, ⌊>Arthur C. Clarke>⌋ was visiting his friend and colleague John Pierce at the Bell Labs Murray Hill facility.@@@@1@19@@oe@26-8-2013
1000008000420@unknown@formal@none@1@S@Clarke was so impressed by the demonstration that he used it in the climactic scene of his screenplay for his novel ⌊/⌊>2001: A Space Odyssey>⌋/⌋, where the ⌊>HAL 9000>⌋ computer sings the same song as it is being put to sleep by astronaut ⌊>Dave Bowman>⌋.@@@@1@45@@oe@26-8-2013
1000008000430@unknown@formal@none@1@S@Despite the success of purely electronic speech synthesis, research is still being conducted into mechanical speech synthesizers.@@@@1@17@@oe@26-8-2013
1000008000440@unknown@formal@none@1@S@⌊=Synthesizer technologies¦2=⌋@@@@1@2@@oe@26-8-2013
1000008000450@unknown@formal@none@1@S@The most important qualities of a speech synthesis system are ⌊/naturalness/⌋ and ⌊/⌊>Intelligibility>⌋/⌋.@@@@1@13@@oe@26-8-2013
1000008000460@unknown@formal@none@1@S@Naturalness describes how closely the output sounds like human speech, while intelligibility is the ease with which the output is understood.@@@@1@21@@oe@26-8-2013
1000008000470@unknown@formal@none@1@S@The ideal speech synthesizer is both natural and intelligible.@@@@1@9@@oe@26-8-2013
1000008000480@unknown@formal@none@1@S@Speech synthesis systems usually try to maximize both characteristics.@@@@1@9@@oe@26-8-2013
1000008000490@unknown@formal@none@1@S@The two primary technologies for generating synthetic speech waveforms are ⌊/concatenative synthesis/⌋ and ⌊/⌊>formant>⌋ synthesis/⌋.@@@@1@15@@oe@26-8-2013
1000008000500@unknown@formal@none@1@S@Each technology has strengths and weaknesses, and the intended uses of a synthesis system will typically determine which approach is used.@@@@1@21@@oe@26-8-2013
1000008000510@unknown@formal@none@1@S@⌊=Concatenative synthesis¦3=⌋@@@@1@2@@oe@26-8-2013
1000008000520@unknown@formal@none@1@S@Concatenative synthesis is based on the ⌊>concatenation>⌋ (or stringing together) of segments of recorded speech.@@@@1@15@@oe@26-8-2013
1000008000530@unknown@formal@none@1@S@Generally, concatenative synthesis produces the most natural-sounding synthesized speech.@@@@1@9@@oe@26-8-2013
1000008000540@unknown@formal@none@1@S@However, differences between natural variations in speech and the nature of the automated techniques for segmenting the waveforms sometimes result in audible glitches in the output.@@@@1@26@@oe@26-8-2013
1000008000550@unknown@formal@none@1@S@There are three main sub-types of concatenative synthesis.@@@@1@8@@oe@26-8-2013
1000008000560@unknown@formal@none@1@S@⌊=Unit selection synthesis¦4=⌋@@@@1@3@@oe@26-8-2013
1000008000570@unknown@formal@none@1@S@Unit selection synthesis uses large ⌊>database>⌋s of recorded speech.@@@@1@9@@oe@26-8-2013
1000008000580@unknown@formal@none@1@S@During database creation, each recorded utterance is segmented into some or all of the following: individual ⌊>phone>⌋s, ⌊>diphone>⌋s, half-phones, ⌊>syllable>⌋s, ⌊>morpheme>⌋s, ⌊>word>⌋s, ⌊>phrase>⌋s, and ⌊>sentence>⌋s.@@@@1@25@@oe@26-8-2013
1000008000590@unknown@formal@none@1@S@Typically, the division into segments is done using a specially modified ⌊>speech recognizer>⌋ set to a "forced alignment" mode with some manual correction afterward, using visual representations such as the ⌊>waveform>⌋ and ⌊>spectrogram>⌋.@@@@1@33@@oe@26-8-2013
1000008000600@unknown@formal@none@1@S@An ⌊>index>⌋ of the units in the speech database is then created based on the segmentation and acoustic parameters like the ⌊>fundamental frequency>⌋ (⌊>pitch>⌋), duration, position in the syllable, and neighboring phones.@@@@1@32@@oe@26-8-2013
1000008000610@unknown@formal@none@1@S@At ⌊>runtime>⌋, the desired target utterance is created by determining the best chain of candidate units from the database (unit selection).@@@@1@21@@oe@26-8-2013
1000008000620@unknown@formal@none@1@S@This process is typically achieved using a specially weighted ⌊>decision tree>⌋.@@@@1@11@@oe@26-8-2013
1000008000630@unknown@formal@none@1@S@Unit selection provides the greatest naturalness, because it applies only a small amount of ⌊>digital signal processing>⌋ (DSP) to the recorded speech.@@@@1@22@@oe@26-8-2013
1000008000640@unknown@formal@none@1@S@DSP often makes recorded speech sound less natural, although some systems use a small amount of signal processing at the point of concatenation to smooth the waveform.@@@@1@27@@oe@26-8-2013
1000008000650@unknown@formal@none@1@S@The output from the best unit-selection systems is often indistinguishable from real human voices, especially in contexts for which the TTS system has been tuned.@@@@1@25@@oe@26-8-2013
1000008000660@unknown@formal@none@1@S@However, maximum naturalness typically require unit-selection speech databases to be very large, in some systems ranging into the ⌊>gigabyte>⌋s of recorded data, representing dozens of hours of speech.@@@@1@28@@oe@26-8-2013
1000008000670@unknown@formal@none@1@S@Also, unit selection algorithms have been known to select segments from a place that results in less than ideal synthesis (e.g. minor words become unclear) even when a better choice exists in the database.@@@@1@34@@oe@26-8-2013
1000008000680@unknown@formal@none@1@S@⌊=Diphone synthesis¦4=⌋@@@@1@2@@oe@26-8-2013
1000008000690@unknown@formal@none@1@S@Diphone synthesis uses a minimal speech database containing all the ⌊>diphone>⌋s (sound-to-sound transitions) occurring in a language.@@@@1@17@@oe@26-8-2013
1000008000700@unknown@formal@none@1@S@The number of diphones depends on the ⌊>phonotactics>⌋ of the language: for example, Spanish has about 800 diphones, and German about 2500.@@@@1@22@@oe@26-8-2013
1000008000710@unknown@formal@none@1@S@In diphone synthesis, only one example of each diphone is contained in the speech database.@@@@1@15@@oe@26-8-2013
1000008000720@unknown@formal@none@1@S@At runtime, the target ⌊>prosody>⌋ of a sentence is superimposed on these minimal units by means of ⌊>digital signal processing>⌋ techniques such as ⌊>linear predictive coding>⌋, ⌊>PSOLA>⌋ or ⌊>MBROLA>⌋.@@@@1@29@@oe@26-8-2013
1000008000730@unknown@formal@none@1@S@The quality of the resulting speech is generally worse than that of unit-selection systems, but more natural-sounding than the output of formant synthesizers.@@@@1@23@@oe@26-8-2013
1000008000740@unknown@formal@none@1@S@Diphone synthesis suffers from the sonic glitches of concatenative synthesis and the robotic-sounding nature of formant synthesis, and has few of the advantages of either approach other than small size.@@@@1@30@@oe@26-8-2013
1000008000750@unknown@formal@none@1@S@As such, its use in commercial applications is declining, although it continues to be used in research because there are a number of freely available software implementations.@@@@1@27@@oe@26-8-2013
1000008000760@unknown@formal@none@1@S@⌊=Domain-specific synthesis¦4=⌋@@@@1@2@@oe@26-8-2013
1000008000770@unknown@formal@none@1@S@Domain-specific synthesis concatenates prerecorded words and phrases to create complete utterances.@@@@1@11@@oe@26-8-2013
1000008000780@unknown@formal@none@1@S@It is used in applications where the variety of texts the system will output is limited to a particular domain, like transit schedule announcements or weather reports.@@@@1@27@@oe@26-8-2013
1000008000790@unknown@formal@none@1@S@The technology is very simple to implement, and has been in commercial use for a long time, in devices like talking clocks and calculators.@@@@1@24@@oe@26-8-2013
1000008000800@unknown@formal@none@1@S@The level of naturalness of these systems can be very high because the variety of sentence types is limited, and they closely match the prosody and intonation of the original recordings.@@@@1@31@@oe@26-8-2013
1000008000810@unknown@formal@none@1@S@Because these systems are limited by the words and phrases in their databases, they are not general-purpose and can only synthesize the combinations of words and phrases with which they have been preprogrammed.@@@@1@33@@oe@26-8-2013
1000008000820@unknown@formal@none@1@S@The blending of words within naturally spoken language however can still cause problems unless the many variations are taken into account.@@@@1@21@@oe@26-8-2013
1000008000830@unknown@formal@none@1@S@For example, in ⌊>non-rhotic>⌋ dialects of English the <r> in words like <clear> ⌊λ/ˈkliːə/¦/ˈkliːə/¦IPAλ⌋ is usually only pronounced when the following word has a vowel as its first letter (e.g. <clear out> is realized as ⌊λ/ˌkliːəɹˈɑʊt/¦/ˌkliːəɹˈɑʊt/¦IPAλ⌋).@@@@1@36@@oe@26-8-2013
1000008000840@unknown@formal@none@1@S@Likewise in ⌊>French>⌋, many final consonants become no longer silent if followed by a word that begins with a vowel, an effect called ⌊>liaison>⌋.@@@@1@24@@oe@26-8-2013
1000008000850@unknown@formal@none@1@S@This ⌊>alternation>⌋ cannot be reproduced by a simple word-concatenation system, which would require additional complexity to be ⌊>context-sensitive>⌋.@@@@1@18@@oe@26-8-2013
1000008000860@unknown@formal@none@1@S@⌊=Formant synthesis¦3=⌋@@@@1@2@@oe@26-8-2013
1000008000870@unknown@formal@none@1@S@⌊>Formant>⌋ synthesis does not use human speech samples at runtime.@@@@1@10@@oe@26-8-2013
1000008000880@unknown@formal@none@1@S@Instead, the synthesized speech output is created using an acoustic model.@@@@1@11@@oe@26-8-2013
1000008000890@unknown@formal@none@1@S@Parameters such as ⌊>fundamental frequency>⌋, ⌊>voicing>⌋, and ⌊>noise>⌋ levels are varied over time to create a ⌊>waveform>⌋ of artificial speech.@@@@1@20@@oe@26-8-2013
1000008000900@unknown@formal@none@1@S@This method is sometimes called ⌊/rules-based synthesis/⌋; however, many concatenative systems also have rules-based components.@@@@1@15@@oe@26-8-2013
1000008000910@unknown@formal@none@1@S@Many systems based on formant synthesis technology generate artificial, robotic-sounding speech that would never be mistaken for human speech.@@@@1@19@@oe@26-8-2013
1000008000920@unknown@formal@none@1@S@However, maximum naturalness is not always the goal of a speech synthesis system, and formant synthesis systems have advantages over concatenative systems.@@@@1@22@@oe@26-8-2013
1000008000930@unknown@formal@none@1@S@Formant-synthesized speech can be reliably intelligible, even at very high speeds, avoiding the acoustic glitches that commonly plague concatenative systems.@@@@1@20@@oe@26-8-2013
1000008000940@unknown@formal@none@1@S@High-speed synthesized speech is used by the visually impaired to quickly navigate computers using a ⌊>screen reader>⌋.@@@@1@17@@oe@26-8-2013
1000008000950@unknown@formal@none@1@S@Formant synthesizers are usually smaller programs than concatenative systems because they do not have a database of speech samples.@@@@1@19@@oe@26-8-2013
1000008000960@unknown@formal@none@1@S@They can therefore be used in ⌊>embedded system>⌋s, where ⌊>memory>⌋ and ⌊>microprocessor>⌋ power are especially limited.@@@@1@16@@oe@26-8-2013
1000008000970@unknown@formal@none@1@S@Because formant-based systems have complete control of all aspects of the output speech, a wide variety of prosodies and ⌊>intonation>⌋s can be output, conveying not just questions and statements, but a variety of emotions and tones of voice.@@@@1@38@@oe@26-8-2013
1000008000980@unknown@formal@none@1@S@Examples of non-real-time but highly accurate intonation control in formant synthesis include the work done in the late 1970s for the ⌊>Texas Instruments>⌋ toy ⌊>Speak & Spell>⌋, and in the early 1980s ⌊>Sega>⌋ ⌊>arcade>⌋ machines.@@@@1@35@@oe@26-8-2013
1000008000990@unknown@formal@none@1@S@Creating proper intonation for these projects was painstaking, and the results have yet to be matched by real-time text-to-speech interfaces.@@@@1@20@@oe@26-8-2013
1000008001000@unknown@formal@none@1@S@⌊=Articulatory synthesis¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001010@unknown@formal@none@1@S@⌊>Articulatory synthesis>⌋ refers to computational techniques for synthesizing speech based on models of the human ⌊>vocal tract>⌋ and the articulation processes occurring there.@@@@1@23@@oe@26-8-2013
1000008001020@unknown@formal@none@1@S@The first articulatory synthesizer regularly used for laboratory experiments was developed at ⌊>Haskins Laboratories>⌋ in the mid-1970s by ⌊>Philip Rubin>⌋, Tom Baer, and Paul Mermelstein.@@@@1@25@@oe@26-8-2013
1000008001030@unknown@formal@none@1@S@This synthesizer, known as ASY, was based on vocal tract models developed at ⌊>Bell Laboratories>⌋ in the 1960s and 1970s by Paul Mermelstein, Cecil Coker, and colleagues.@@@@1@27@@oe@26-8-2013
1000008001040@unknown@formal@none@1@S@Until recently, articulatory synthesis models have not been incorporated into commercial speech synthesis systems.@@@@1@14@@oe@26-8-2013
1000008001050@unknown@formal@none@1@S@A notable exception is the ⌊>NeXT>⌋-based system originally developed and marketed by Trillium Sound Research, a spin-off company of the ⌊>University of Calgary>⌋, where much of the original research was conducted.@@@@1@31@@oe@26-8-2013
1000008001060@unknown@formal@none@1@S@Following the demise of the various incarnations of NeXT (started by ⌊>Steve Jobs>⌋ in the late 1980s and merged with Apple Computer in 1997), the Trillium software was published under the ⌊>GNU General Public License>⌋, with work continuing as ⌊/gnuspeech/⌋.@@@@1@40@@oe@26-8-2013
1000008001070@unknown@formal@none@1@S@The system, first marketed in 1994, provides full articulatory-based text-to-speech conversion using a waveguide or transmission-line analog of the human oral and nasal tracts controlled by Carré's "distinctive region model".@@@@1@30@@oe@26-8-2013
1000008001080@unknown@formal@none@1@S@⌊=HMM-based synthesis¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001090@unknown@formal@none@1@S@HMM-based synthesis is a synthesis method based on ⌊>hidden Markov model>⌋s.@@@@1@11@@oe@26-8-2013
1000008001100@unknown@formal@none@1@S@In this system, the ⌊>frequency spectrum>⌋ (⌊>vocal tract>⌋), ⌊>fundamental frequency>⌋ (vocal source), and duration (⌊>prosody>⌋) of speech are modeled simultaneously by HMMs.@@@@1@22@@oe@26-8-2013
1000008001110@unknown@formal@none@1@S@Speech ⌊>waveforms>⌋ are generated from HMMs themselves based on the ⌊>maximum likelihood>⌋ criterion.@@@@1@13@@oe@26-8-2013
1000008001120@unknown@formal@none@1@S@⌊=Sinewave synthesis¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001130@unknown@formal@none@1@S@⌊>Sinewave synthesis>⌋ is a technique for synthesizing speech by replacing the ⌊>formants>⌋ (main bands of energy) with pure tone whistles.@@@@1@20@@oe@26-8-2013
1000008001140@unknown@formal@none@1@S@⌊=Challenges¦2=⌋@@@@1@1@@oe@26-8-2013
1000008001150@unknown@formal@none@1@S@⌊=Text normalization challenges¦3=⌋@@@@1@3@@oe@26-8-2013
1000008001160@unknown@formal@none@1@S@The process of normalizing text is rarely straightforward.@@@@1@8@@oe@26-8-2013
1000008001170@unknown@formal@none@1@S@Texts are full of ⌊>heteronym>⌋s, ⌊>number>⌋s, and ⌊>abbreviation>⌋s that all require expansion into a phonetic representation.@@@@1@16@@oe@26-8-2013
1000008001180@unknown@formal@none@1@S@There are many spellings in English which are pronounced differently based on context.@@@@1@13@@oe@26-8-2013
1000008001190@unknown@formal@none@1@S@For example, "My latest project is to learn how to better project my voice" contains two pronunciations of "project".@@@@1@19@@oe@26-8-2013
1000008001200@unknown@formal@none@1@S@Most text-to-speech (TTS) systems do not generate semantic representations of their input texts, as processes for doing so are not reliable, well understood, or computationally effective.@@@@1@26@@oe@26-8-2013
1000008001210@unknown@formal@none@1@S@As a result, various ⌊>heuristic>⌋ techniques are used to guess the proper way to disambiguate homographs, like examining neighboring words and using statistics about frequency of occurrence.@@@@1@27@@oe@26-8-2013
1000008001220@unknown@formal@none@1@S@Deciding how to convert numbers is another problem that TTS systems have to address.@@@@1@14@@oe@26-8-2013
1000008001230@unknown@formal@none@1@S@It is a simple programming challenge to convert a number into words, like "1325" becoming "one thousand three hundred twenty-five."@@@@1@20@@oe@26-8-2013
1000008001240@unknown@formal@none@1@S@However, numbers occur in many different contexts; when a year or part of an address, "1325" should likely be read as "thirteen twenty-five", or, when part of a ⌊>social security number>⌋, as "one three two five".@@@@1@36@@oe@26-8-2013
1000008001250@unknown@formal@none@1@S@A TTS system can often infer how to expand a number based on surrounding words, numbers, and punctuation, and sometimes the system provides a way to specify the context if it is ambiguous.@@@@1@33@@oe@26-8-2013
1000008001260@unknown@formal@none@1@S@Similarly, abbreviations can be ambiguous.@@@@1@5@@oe@26-8-2013
1000008001270@unknown@formal@none@1@S@For example, the abbreviation "in" for "inches" must be differentiated from the word "in", and the address "12 St John St." uses the same abbreviation for both "Saint" and "Street".@@@@1@30@@oe@26-8-2013
1000008001280@unknown@formal@none@1@S@TTS systems with intelligent front ends can make educated guesses about ambiguous abbreviations, while others provide the same result in all cases, resulting in nonsensical (and sometimes comical) outputs.@@@@1@29@@oe@26-8-2013
1000008001290@unknown@formal@none@1@S@⌊=Text-to-phoneme challenges¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001300@unknown@formal@none@1@S@Speech synthesis systems use two basic approaches to determine the pronunciation of a word based on its spelling, a process which is often called text-to-phoneme or grapheme-to-phoneme conversion (⌊>phoneme>⌋ is the term used by linguists to describe distinctive sounds in a language).@@@@1@42@@oe@26-8-2013
1000008001310@unknown@formal@none@1@S@The simplest approach to text-to-phoneme conversion is the dictionary-based approach, where a large dictionary containing all the words of a language and their correct pronunciations is stored by the program.@@@@1@30@@oe@26-8-2013
1000008001320@unknown@formal@none@1@S@Determining the correct pronunciation of each word is a matter of looking up each word in the dictionary and replacing the spelling with the pronunciation specified in the dictionary.@@@@1@29@@oe@26-8-2013
1000008001330@unknown@formal@none@1@S@The other approach is rule-based, in which pronunciation rules are applied to words to determine their pronunciations based on their spellings.@@@@1@21@@oe@26-8-2013
1000008001340@unknown@formal@none@1@S@This is similar to the "sounding out", or ⌊>synthetic phonics>⌋, approach to learning reading.@@@@1@14@@oe@26-8-2013
1000008001350@unknown@formal@none@1@S@Each approach has advantages and drawbacks.@@@@1@6@@oe@26-8-2013
1000008001360@unknown@formal@none@1@S@The dictionary-based approach is quick and accurate, but completely fails if it is given a word which is not in its dictionary.@@@@1@22@@oe@26-8-2013
1000008001370@unknown@formal@none@1@S@As dictionary size grows, so too does the memory space requirements of the synthesis system.@@@@1@15@@oe@26-8-2013
1000008001380@unknown@formal@none@1@S@On the other hand, the rule-based approach works on any input, but the complexity of the rules grows substantially as the system takes into account irregular spellings or pronunciations.@@@@1@29@@oe@26-8-2013
1000008001390@unknown@formal@none@1@S@(Consider that the word "of" is very common in English, yet is the only word in which the letter "f" is pronounced [v].)@@@@1@23@@oe@26-8-2013
1000008001400@unknown@formal@none@1@S@As a result, nearly all speech synthesis systems use a combination of these approaches.@@@@1@14@@oe@26-8-2013
1000008001410@unknown@formal@none@1@S@Some languages, like ⌊>Spanish>⌋, have a very regular writing system, and the prediction of the pronunciation of words based on their spellings is quite successful.@@@@1@25@@oe@26-8-2013
1000008001420@unknown@formal@none@1@S@Speech synthesis systems for such languages often use the rule-based method extensively, resorting to dictionaries only for those few words, like foreign names and borrowings, whose pronunciations are not obvious from their spellings.@@@@1@33@@oe@26-8-2013
1000008001430@unknown@formal@none@1@S@On the other hand, speech synthesis systems for languages like ⌊>English>⌋, which have extremely irregular spelling systems, are more likely to rely on dictionaries, and to use rule-based methods only for unusual words, or words that aren't in their dictionaries.@@@@1@40@@oe@26-8-2013
1000008001440@unknown@formal@none@1@S@⌊=Evaluation challenges¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001450@unknown@formal@none@1@S@It is very difficult to evaluate speech synthesis systems consistently because there is no subjective criterion and usually different organizations use different speech data.@@@@1@24@@oe@26-8-2013
1000008001460@unknown@formal@none@1@S@The quality of a speech synthesis system highly depends on the quality of recording.@@@@1@14@@oe@26-8-2013
1000008001470@unknown@formal@none@1@S@Therefore, evaluating speech synthesis systems is almost the same as evaluating the recording skills.@@@@1@14@@oe@26-8-2013
1000008001480@unknown@formal@none@1@S@Recently researchers start evaluating speech synthesis systems using the common speech dataset.@@@@1@12@@oe@26-8-2013
1000008001490@unknown@formal@none@1@S@This may help people to compare the difference between technologies rather than recordings.@@@@1@13@@oe@26-8-2013
1000008001500@unknown@formal@none@1@S@⌊=Prosodics and emotional content¦3=⌋@@@@1@4@@oe@26-8-2013
1000008001510@unknown@formal@none@1@S@A recent study reported in the journal "⌊∗Speech Communication∗⌋" by Amy Drahota and colleagues at the ⌊>University of Portsmouth>⌋, ⌊>UK>⌋, reported that listeners to voice recordings could determine, at better than chance levels, whether or not the speaker was smiling.@@@@1@40@@oe@26-8-2013
1000008001520@unknown@formal@none@1@S@It was suggested that identification of the vocal features which signal emotional content may be used to help make synthesized speech sound more natural.@@@@1@24@@oe@26-8-2013
1000008001530@unknown@formal@none@1@S@⌊=Computer operating systems or outlets with speech synthesis¦2=⌋@@@@1@8@@oe@26-8-2013
1000008001540@unknown@formal@none@1@S@⌊=Apple¦3=⌋@@@@1@1@@oe@26-8-2013
1000008001550@unknown@formal@none@1@S@The first speech system integrated into an ⌊>operating system>⌋ was ⌊>Apple Computer>⌋'s ⌊>MacInTalk>⌋ in 1984.@@@@1@15@@oe@26-8-2013
1000008001560@unknown@formal@none@1@S@Since the 1980s Macintosh Computers offered text to speech capabilities through The MacinTalk software.@@@@1@14@@oe@26-8-2013
1000008001570@unknown@formal@none@1@S@In the early 1990s Apple expanded its capabilities offering system wide text-to-speech support.@@@@1@13@@oe@26-8-2013
1000008001580@unknown@formal@none@1@S@With the introduction of faster PowerPC based computers they included higher quality voice sampling.@@@@1@14@@oe@26-8-2013
1000008001590@unknown@formal@none@1@S@Apple also introduced ⌊>speech recognition>⌋ into its systems which provided a fluid command set.@@@@1@14@@oe@26-8-2013
1000008001600@unknown@formal@none@1@S@More recently, Apple has added sample-based voices.@@@@1@7@@oe@26-8-2013
1000008001610@unknown@formal@none@1@S@Starting as a curiosity, the speech system of Apple ⌊>Macintosh>⌋ has evolved into a cutting edge fully-supported program, ⌊>PlainTalk>⌋, for people with vision problems.@@@@1@24@@oe@26-8-2013
1000008001620@unknown@formal@none@1@S@⌊>VoiceOver>⌋ was included in Mac OS Tiger and more recently Mac OS Leopard.@@@@1@13@@oe@26-8-2013
1000008001630@unknown@formal@none@1@S@The voice shipping with Mac OS X 10.5 ("Leopard") is called "Alex" and features the taking of realistic-sounding breaths between sentences, as well as improved clarity at high read rates.@@@@1@30@@oe@26-8-2013
1000008001640@unknown@formal@none@1@S@⌊=AmigaOS¦3=⌋@@@@1@1@@oe@26-8-2013
1000008001650@unknown@formal@none@1@S@The second operating system with advanced speech synthesis capabilities was ⌊>AmigaOS>⌋, introduced in 1985.@@@@1@14@@oe@26-8-2013
1000008001660@unknown@formal@none@1@S@The voice synthesis was licensed by ⌊>Commodore International>⌋ from a third-party software house (Don't Ask Software, now Softvoice, Inc.) and it featured a complete system of voice emulation, with both male and female voices and "stress" indicator markers, made possible by advanced features of the ⌊>Amiga>⌋ hardware audio ⌊>chipset>⌋.@@@@1@49@@oe@26-8-2013
1000008001670@unknown@formal@none@1@S@It was divided into a narrator device and a translator library.@@@@1@11@@oe@26-8-2013
1000008001680@unknown@formal@none@1@S@Amiga ⌊>Speak Handler>⌋ featured a text-to-speech translator.@@@@1@7@@oe@26-8-2013
1000008001690@unknown@formal@none@1@S@AmigaOS considered speech synthesis a virtual hardware device, so the user could even redirect console output to it.@@@@1@18@@oe@26-8-2013
1000008001700@unknown@formal@none@1@S@Some Amiga programs, such as word processors, made extensive use of the speech system.@@@@1@14@@oe@26-8-2013
1000008001710@unknown@formal@none@1@S@⌊=Microsoft Windows¦3=⌋@@@@1@2@@oe@26-8-2013
1000008001720@unknown@formal@none@1@S@Modern ⌊>Windows>⌋ systems use ⌊>SAPI4>⌋- and ⌊>SAPI5>⌋-based speech systems that include a ⌊>speech recognition>⌋ engine (SRE).@@@@1@16@@oe@26-8-2013
1000008001730@unknown@formal@none@1@S@SAPI 4.0 was available on Microsoft-based operating systems as a third-party add-on for systems like ⌊>Windows 95>⌋ and ⌊>Windows 98>⌋.@@@@1@20@@oe@26-8-2013
1000008001740@unknown@formal@none@1@S@⌊>Windows 2000>⌋ added a speech synthesis program called ⌊>Narrator>⌋, directly available to users.@@@@1@13@@oe@26-8-2013
1000008001750@unknown@formal@none@1@S@All Windows-compatible programs could make use of speech synthesis features, available through menus once installed on the system.@@@@1@18@@oe@26-8-2013
1000008001760@unknown@formal@none@1@S@⌊>Microsoft Speech Server>⌋ is a complete package for voice synthesis and recognition, for commercial applications such as ⌊>call centers>⌋.@@@@1@19@@oe@26-8-2013
1000008001770@unknown@formal@none@1@S@⌊=Internet¦3=⌋@@@@1@1@@oe@26-8-2013
1000008001780@unknown@formal@none@1@S@Currently, there are a number of ⌊>applications>⌋, ⌊>plugin>⌋s and ⌊>gadget>⌋s that can read messages directly from an ⌊>e-mail client>⌋ and web pages from a ⌊>web browser>⌋.@@@@1@26@@oe@26-8-2013
1000008001790@unknown@formal@none@1@S@Some specialized ⌊>software>⌋ can narrate ⌊>RSS-feeds>⌋.@@@@1@6@@oe@26-8-2013
1000008001800@unknown@formal@none@1@S@On one hand, online RSS-narrators simplify information delivery by allowing users to listen to their favourite news sources and to convert them to ⌊>podcast>⌋s.@@@@1@24@@oe@26-8-2013
1000008001810@unknown@formal@none@1@S@On the other hand, on-line RSS-readers are available on almost any ⌊>PC>⌋ connected to the Internet.@@@@1@16@@oe@26-8-2013
1000008001820@unknown@formal@none@1@S@Users can download generated audio files to portable devices, e.g. with a help of ⌊>podcast>⌋ receiver, and listen to them while walking, jogging or commuting to work.@@@@1@27@@oe@26-8-2013
1000008001830@unknown@formal@none@1@S@A growing field in internet based TTS technology is web-based assistive technology, e.g. Talklets.@@@@1@14@@oe@26-8-2013
1000008001840@unknown@formal@none@1@S@This web based approach to a traditionally locally installed form of software application can afford many of those requiring software for accessibility reason, the ability to access web content from public machines, or those belonging to others.@@@@1@37@@oe@26-8-2013
1000008001850@unknown@formal@none@1@S@While responsiveness is not as immediate as that of applications installed locally, the 'access anywhere' nature of it is the key benefit to this approach.@@@@1@25@@oe@26-8-2013
1000008001860@unknown@formal@none@1@S@⌊=Others¦3=⌋@@@@1@1@@oe@26-8-2013
1000008001870@unknown@formal@none@1@S@⌊•⌊#Some models of Texas Instruments home computers produced in 1979 and 1981 (⌊>Texas Instruments TI-99/4 and TI-99/4A>⌋) were capable of text-to-phoneme synthesis or reciting complete words and phrases (text-to-dictionary), using a very popular Speech Synthesizer peripheral.@@@@1@36@@oe@26-8-2013
1000008001880@unknown@formal@none@1@S@TI used a proprietary ⌊>codec>⌋ to embed complete spoken phrases into applications, primarily video games.#⌋@@@@1@15@@oe@26-8-2013
1000008001890@unknown@formal@none@1@S@⌊#Systems that operate on free and open source software systems including ⌊>GNU/Linux>⌋ are various, and include ⌊>open-source>⌋ programs such as the ⌊>Festival Speech Synthesis System>⌋ which uses diphone-based synthesis (and can use a limited number of ⌊>MBROLA>⌋ voices), and gnuspeech which uses articulatory synthesis from the ⌊>Free Software Foundation>⌋.@@@@1@49@@oe@26-8-2013
1000008001900@unknown@formal@none@1@S@Other commercial vendor software also runs on GNU/Linux.#⌋@@@@1@8@@oe@26-8-2013
1000008001910@unknown@formal@none@1@S@⌊#Several commercial companies are also developing speech synthesis systems (this list is reporting them just for the sake of information, not endorsing any specific product): ⌊> Acapela Group>⌋, ⌊>AT&T>⌋, ⌊>Cepstral>⌋, ⌊>DECtalk>⌋, ⌊>IBM ViaVoice>⌋, ⌊>IVONA TTS>⌋, ⌊>Loquendo TTS>⌋, ⌊> NeoSpeech TTS>⌋, ⌊>Nuance Communications>⌋, Rhetorical Systems, ⌊> SVOX>⌋ and ⌊> YAKiToMe!>⌋.#⌋@@@@1@49@@oe@26-8-2013
1000008001920@unknown@formal@none@1@S@⌊#Companies which developed speech synthesis systems but which are no longer in this business include BeST Speech (bought by L&H), ⌊>Lernout & Hauspie>⌋ (bankrupt), ⌊>SpeechWorks>⌋ (bought by Nuance)#⌋•⌋@@@@1@28@@oe@26-8-2013
1000008001930@unknown@formal@none@1@S@⌊=Speech synthesis markup languages¦2=⌋@@@@1@4@@oe@26-8-2013
1000008001940@unknown@formal@none@1@S@A number of ⌊>markup language>⌋s have been established for the rendition of text as speech in an ⌊>XML>⌋-compliant format.@@@@1@19@@oe@26-8-2013
1000008001950@unknown@formal@none@1@S@The most recent is ⌊>Speech Synthesis Markup Language>⌋ (SSML), which became a ⌊>W3C recommendation>⌋ in 2004.@@@@1@16@@oe@26-8-2013
1000008001960@unknown@formal@none@1@S@Older speech synthesis markup languages include Java Speech Markup Language (⌊>JSML>⌋) and ⌊>SABLE>⌋.@@@@1@13@@oe@26-8-2013
1000008001970@unknown@formal@none@1@S@Although each of these was proposed as a standard, none of them has been widely adopted.@@@@1@16@@oe@26-8-2013
1000008001980@unknown@formal@none@1@S@Speech synthesis markup languages are distinguished from dialogue markup languages.@@@@1@10@@oe@26-8-2013
1000008001990@unknown@formal@none@1@S@⌊>VoiceXML>⌋, for example, includes tags related to speech recognition, dialogue management and touchtone dialing, in addition to text-to-speech markup.@@@@1@19@@oe@26-8-2013
1000008002000@unknown@formal@none@1@S@⌊=Applications¦2=⌋@@@@1@1@@oe@26-8-2013
1000008002010@unknown@formal@none@1@S@⌊=Accessibility¦3=⌋@@@@1@1@@oe@26-8-2013
1000008002020@unknown@formal@none@1@S@Speech synthesis has long been a vital ⌊>assistive technology>⌋ tool and its application in this area is significant and widespread.@@@@1@20@@oe@26-8-2013
1000008002030@unknown@formal@none@1@S@It allows environmental barriers to be removed for people with a wide range of disabilities.@@@@1@15@@oe@26-8-2013
1000008002040@unknown@formal@none@1@S@The longest application has been in the use of ⌊>screenreaders>⌋ for people with ⌊>visual impairment>⌋, but text-to-speech systems are now commonly used by people with ⌊>dyslexia>⌋ and other reading difficulties as well as by pre-literate youngsters.@@@@1@36@@oe@26-8-2013
1000008002050@unknown@formal@none@1@S@They are also frequently employed to aid those with severe ⌊>speech impairment>⌋ usually through a dedicated ⌊>voice output communication aid>⌋.@@@@1@20@@oe@26-8-2013
1000008002060@unknown@formal@none@1@S@⌊=News service¦3=⌋@@@@1@2@@oe@26-8-2013
1000008002070@unknown@formal@none@1@S@Sites such as ⌊>Ananova>⌋ have used speech synthesis to convert written news to audio content, which can be used for mobile applications.@@@@1@22@@oe@26-8-2013
1000008002080@unknown@formal@none@1@S@⌊=Entertainment¦3=⌋@@@@1@1@@oe@26-8-2013
1000008002090@unknown@formal@none@1@S@Speech synthesis techniques are used as well in the entertainment productions such as games, anime and similar.@@@@1@17@@oe@26-8-2013
1000008002100@unknown@formal@none@1@S@In 2007, Animo Limited announced the development of a software application package based on its speech synthesis software FineSpeech, explicitly geared towards customers in the entertainment industries, able to generate narration and lines of dialogue according to user specifications.@@@@1@39@@oe@26-8-2013
1000008002110@unknown@formal@none@1@S@Software such as ⌊>Vocaloid>⌋ can generate singing voices via lyrics and melody.@@@@1@12@@oe@26-8-2013
1000008002120@unknown@formal@none@1@S@This is also the aim of the Singing Computer project (which uses the ⌊>GPL>⌋ software ⌊>Lilypond>⌋ and ⌊>Festival>⌋) to help blind people check their lyric input.@@@@1@26@@oe@26-8-2013
1000008100010@unknown@formal@none@1@S@⌊δStatistical classificationδ⌋@@@@1@2@@oe@26-8-2013
1000008100020@unknown@formal@none@1@S@⌊∗Statistical classification∗⌋ is a procedure in which individual items are placed into groups based on quantitative information on one or more characteristics inherent in the items (referred to as traits, variables, characters, etc) and based on a ⌊>training set>⌋ of previously labeled items.@@@@1@43@@oe@26-8-2013
1000008100030@unknown@formal@none@1@S@Formally, the problem can be stated as follows: given training data ⌊×\\{(\\mathbf{x_1},y_1),\\dots,(\\mathbf{x_n}, y_n)\\}×⌋ produce a classifier ⌊×h:\\mathcal{X}\\rightarrow\\mathcal{Y}×⌋ which maps an object ⌊×\\mathbf{x} \\in \\mathcal{X}×⌋ to its classification label ⌊×y \\in \\mathcal{Y}×⌋.@@@@1@31@@oe@26-8-2013
1000008100040@unknown@formal@none@1@S@For example, if the problem is filtering spam, then ⌊×\\mathbf{x_i}×⌋ is some representation of an email and ⌊×y×⌋ is either "Spam" or "Non-Spam".@@@@1@23@@oe@26-8-2013
1000008100050@unknown@formal@none@1@S@Statistical classification algorithms are typically used in ⌊>pattern recognition>⌋ systems.@@@@1@10@@oe@26-8-2013
1000008100060@unknown@formal@none@1@S@⌊∗Note:∗⌋ in ⌊>community ecology>⌋, the term "classification" is synonymous with what is commonly known (in ⌊>machine learning>⌋) as ⌊>clustering>⌋.@@@@1@19@@oe@26-8-2013
1000008100070@unknown@formal@none@1@S@See that article for more information about purely ⌊>unsupervised>⌋ techniques.@@@@1@10@@oe@26-8-2013
1000008100080@unknown@formal@none@1@S@⌊•⌊#The second problem is to consider classification as an ⌊>estimation>⌋ problem, where the goal is to estimate a function of the form#⌋•⌋@@@@1@22@@oe@26-8-2013
1000008100090@unknown@formal@none@1@S@⌊⇥⌊×P({\\rm class}|{\\vec x}) = f\\left(\\vec x;\\vec \\theta\\right)×⌋ ⇥⌋where the feature vector input is ⌊×\\vec x×⌋, and the function ⌊/f/⌋ is typically parameterized by some parameters ⌊×\\vec \\theta×⌋.@@@@1@27@@oe@26-8-2013
1000008100100@unknown@formal@none@1@S@In the ⌊>Bayesian>⌋ approach to this problem, instead of choosing a single parameter vector ⌊×\\vec \\theta×⌋, the result is integrated over all possible thetas, with the thetas weighted by how likely they are given the training data ⌊/D/⌋:@@@@1@38@@oe@26-8-2013
1000008100110@unknown@formal@none@1@S@⌊⇥⌊×P({\\rm class}|{\\vec x}) = \\int f\\left(\\vec x;\\vec \\theta\\right)P(\\vec \\theta|D) d\\vec \\theta×⌋⇥⌋@@@@1@11@@oe@26-8-2013
1000008100120@unknown@formal@none@1@S@⌊•⌊#The third problem is related to the second, but the problem is to estimate the ⌊>class-conditional probabilities>⌋ ⌊×P(\\vec x|{\\rm class})×⌋ and then use ⌊>Bayes' rule>⌋ to produce the class probability as in the second problem.#⌋•⌋@@@@1@35@@oe@26-8-2013
1000008100130@unknown@formal@none@1@S@Examples of classification algorithms include:@@@@1@5@@oe@26-8-2013
1000008100140@unknown@formal@none@1@S@⌊•⌊#⌊>Linear classifier>⌋s@@@@1@2@@oe@26-8-2013
1000008100150@unknown@formal@none@1@S@⌊•⌊#⌊>Fisher's linear discriminant>⌋@@@@1@3@@oe@26-8-2013
1000008100160@unknown@formal@none@1@S@⌊>Logistic regression>⌋@@@@1@2@@oe@26-8-2013
1000008100170@unknown@formal@none@1@S@⌊>Naive Bayes classifier>⌋@@@@1@3@@oe@26-8-2013
1000008100180@unknown@formal@none@1@S@⌊>Perceptron>⌋#⌋@@@@1@1@@oe@26-8-2013
1000008100190@unknown@formal@none@1@S@⌊#⌊>Support vector machine>⌋s#⌋•⌋#⌋@@@@1@3@@oe@26-8-2013
1000008100200@unknown@formal@none@1@S@⌊#⌊>Quadratic classifier>⌋s#⌋@@@@1@2@@oe@26-8-2013
1000008100210@unknown@formal@none@1@S@⌊#⌊>k-nearest neighbor>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008100220@unknown@formal@none@1@S@⌊#⌊>Boosting>⌋#⌋@@@@1@1@@oe@26-8-2013
1000008100230@unknown@formal@none@1@S@⌊#⌊>Decision tree>⌋s@@@@1@2@@oe@26-8-2013
1000008100240@unknown@formal@none@1@S@⌊•⌊#⌊>Random forest>⌋s#⌋•⌋#⌋@@@@1@2@@oe@26-8-2013
1000008100250@unknown@formal@none@1@S@⌊#⌊>Neural network>⌋s#⌋@@@@1@2@@oe@26-8-2013
1000008100260@unknown@formal@none@1@S@⌊#⌊>Bayesian network>⌋s@@@@1@2@@oe@26-8-2013
1000008100270@unknown@formal@none@1@S@⌊>Hidden Markov model>⌋s#⌋•⌋@@@@1@3@@oe@26-8-2013
1000008100280@unknown@formal@none@1@S@An intriguing problem in pattern recognition yet to be solved is the relationship between the problem to be solved (data to be classified) and the performance of various pattern recognition algorithms (classifiers).@@@@1@32@@oe@26-8-2013
1000008100290@unknown@formal@none@1@S@Van der Walt and Barnard (see reference section) investigated very specific artificial data sets to determine conditions under which certain classifiers perform better and worse than others.@@@@1@27@@oe@26-8-2013
1000008100300@unknown@formal@none@1@S@Classifier performance depends greatly on the characteristics of the data to be classified.@@@@1@13@@oe@26-8-2013
1000008100310@unknown@formal@none@1@S@There is no single classifier that works best on all given problems (a phenomenon that may be explained by the ⌊>No-free-lunch theorem>⌋).@@@@1@22@@oe@26-8-2013
1000008100320@unknown@formal@none@1@S@Various empirical tests have been performed to compare classifier performance and to find the characteristics of data that determine classifier performance.@@@@1@21@@oe@26-8-2013
1000008100330@unknown@formal@none@1@S@Determining a suitable classifier for a given problem is however still more an art than a science.@@@@1@17@@oe@26-8-2013
1000008100340@unknown@formal@none@1@S@The most widely used classifiers are the ⌊>Neural Network>⌋ (Multi-layer Perceptron), ⌊>Support Vector Machines>⌋, ⌊>k-Nearest Neighbours>⌋, Gaussian Mixture Model, Gaussian, ⌊>Naive Bayes>⌋, ⌊>Decision Tree>⌋ and ⌊>RBF>⌋ classifiers.@@@@1@27@@oe@26-8-2013
1000008100350@unknown@formal@none@1@S@⌊=Evaluation¦2=⌋@@@@1@1@@oe@26-8-2013
1000008100360@unknown@formal@none@1@S@The measures ⌊>Precision and Recall>⌋ are popular metrics used to evaluate the quality of a classification system.@@@@1@17@@oe@26-8-2013
1000008100370@unknown@formal@none@1@S@More recently, ⌊>Receiver Operating Characteristic>⌋ (ROC) curves have been used to evaluate the tradeoff between true- and false-positive rates of classification algorithms.@@@@1@22@@oe@26-8-2013
1000008200010@unknown@formal@none@1@S@⌊δStatistical machine translationδ⌋@@@@1@3@@oe@26-8-2013
1000008200020@unknown@formal@none@1@S@⌊∗Statistical machine translation∗⌋ (⌊∗SMT∗⌋) is a ⌊>machine translation>⌋ paradigm where translations are generated on the basis of statistical models whose parameters are derived from the analysis of bilingual ⌊>text corpora>⌋.@@@@1@30@@oe@26-8-2013
1000008200030@unknown@formal@none@1@S@The statistical approach contrasts with the rule-based approaches to ⌊>machine translation>⌋ as well as with ⌊>example-based machine translation>⌋.@@@@1@18@@oe@26-8-2013
1000008200040@unknown@formal@none@1@S@The first ideas of statistical machine translation were introduced by ⌊>Warren Weaver>⌋ in 1949, including the ideas of applying ⌊>Claude Shannon>⌋'s ⌊>information theory>⌋.@@@@1@23@@oe@26-8-2013
1000008200050@unknown@formal@none@1@S@Statistical machine translation was re-introduced in 1991 by researchers at ⌊>IBM>⌋'s ⌊>Thomas J. Watson Research Center>⌋ and has contributed to the significant resurgence in interest in machine translation in recent years.@@@@1@31@@oe@26-8-2013
1000008200060@unknown@formal@none@1@S@As of 2006, it is by far the most widely-studied machine translation paradigm.@@@@1@13@@oe@26-8-2013
1000008200070@unknown@formal@none@1@S@⌊=Benefits¦2=⌋@@@@1@1@@oe@26-8-2013
1000008200080@unknown@formal@none@1@S@The benefits of statistical machine translation over traditional paradigms that are most often cited are the following:@@@@1@17@@oe@26-8-2013
1000008200090@unknown@formal@none@1@S@⌊•⌊#⌊∗Better use of resources∗⌋@@@@1@4@@oe@26-8-2013
1000008200100@unknown@formal@none@1@S@⌊•⌊#There is a great deal of natural language in machine-readable format.#⌋@@@@1@11@@oe@26-8-2013
1000008200110@unknown@formal@none@1@S@⌊#Generally, SMT systems are not tailored to any specific pair of languages.#⌋@@@@1@12@@oe@26-8-2013
1000008200120@unknown@formal@none@1@S@⌊#Rule-based translation systems require the manual development of linguistic rules, which can be costly, and which often do not generalize to other languages.#⌋•⌋#⌋@@@@1@23@@oe@26-8-2013
1000008200130@unknown@formal@none@1@S@⌊#⌊∗More natural translations∗⌋#⌋•⌋@@@@1@3@@oe@26-8-2013
1000008200140@unknown@formal@none@1@S@The ideas behind statistical machine translation come out of ⌊>information theory>⌋.@@@@1@11@@oe@26-8-2013
1000008200150@unknown@formal@none@1@S@Essentially, the document is translated on the ⌊>probability>⌋ ⌊×p(e|f)×⌋ that a string ⌊×e×⌋ in native language (for example, English) is the translation of a string ⌊×f×⌋ in foreign language (for example, French).@@@@1@32@@oe@26-8-2013
1000008200160@unknown@formal@none@1@S@Generally, these probabilities are estimated using techniques of ⌊>parameter estimation>⌋.@@@@1@10@@oe@26-8-2013
1000008200170@unknown@formal@none@1@S@The ⌊>Bayes Theorem>⌋ is applied to ⌊×p(e|f)×⌋, the probability that the foreign string produces the native string to get ⌊×p(e|f) \\propto p(f|e) p(e)×⌋, where the ⌊>translation model>⌋ ⌊×p(f|e)×⌋ is the probability that the native string is the translation of the foreign string, and the ⌊>language model>⌋ ⌊×p(e)×⌋ is the probability of seeing that native string.@@@@1@55@@oe@26-8-2013
1000008200180@unknown@formal@none@1@S@Mathematically speaking, finding the best translation ⌊×\\tilde{e}×⌋ is done by picking up the one that gives the highest probability:@@@@1@19@@oe@26-8-2013
1000008200190@unknown@formal@none@1@S@⌊⇥⌊×\\tilde{e} = arg \\max_{e \\in e^*} p(e|f) = arg \\max_{e\\in e^*} p(f|e) p(e) ×⌋.⇥⌋@@@@1@14@@oe@26-8-2013
1000008200200@unknown@formal@none@1@S@For a rigorous implementation of this one would have to perform an exhaustive search by going through all strings ⌊×e^*×⌋ in the native language.@@@@1@24@@oe@26-8-2013
1000008200210@unknown@formal@none@1@S@Performing the search efficiently is the work of a ⌊>machine translation decoder>⌋ that uses the foreign string, heuristics and other methods to limit the search space and at the same time keeping acceptable quality.@@@@1@34@@oe@26-8-2013
1000008200220@unknown@formal@none@1@S@This trade-off between quality and time usage can also be found in ⌊>speech recognition>⌋.@@@@1@14@@oe@26-8-2013
1000008200230@unknown@formal@none@1@S@As the translation systems are not able to store all native strings and their translations, a document is typically translated sentence by sentence, but even this is not enough.@@@@1@29@@oe@26-8-2013
1000008200240@unknown@formal@none@1@S@Language models are typically approximated by smoothed ⌊/n/⌋-gram models, and similar approaches have been applied to translation models, but there is additional complexity due to different sentence lengths and word orders in the languages.@@@@1@34@@oe@26-8-2013
1000008200250@unknown@formal@none@1@S@The statistical translation models were initially ⌊>word>⌋ based (Models 1-5 from ⌊>IBM>⌋), but significant advances were made with the introduction of ⌊>phrase>⌋ based models.@@@@1@24@@oe@26-8-2013
1000008200260@unknown@formal@none@1@S@Recent work has incorporated ⌊>syntax>⌋ or quasi-syntactic structures.@@@@1@8@@oe@26-8-2013
1000008200270@unknown@formal@none@1@S@⌊=Word-based translation¦2=⌋@@@@1@2@@oe@26-8-2013
1000008200280@unknown@formal@none@1@S@In word-based translation, translated elements are words.@@@@1@7@@oe@26-8-2013
1000008200290@unknown@formal@none@1@S@Typically, the number of words in translated sentences are different due to compound words, morphology and idioms.@@@@1@17@@oe@26-8-2013
1000008200300@unknown@formal@none@1@S@The ratio of the lengths of sequences of translated words is called fertility, which tells how many foreign words each native word produces.@@@@1@23@@oe@26-8-2013
1000008200310@unknown@formal@none@1@S@Simple word-based translation is not able to translate language pairs with fertility rates different from one.@@@@1@16@@oe@26-8-2013
1000008200320@unknown@formal@none@1@S@To make word-based translation systems manage, for instance, high fertility rates, the system could be able to map a single word to multiple words, but not vice versa.@@@@1@28@@oe@26-8-2013
1000008200330@unknown@formal@none@1@S@For instance, if we are translating from French to English, each word in English could produce zero or more French words.@@@@1@21@@oe@26-8-2013
1000008200340@unknown@formal@none@1@S@But there's no way to group two English words producing a single French word.@@@@1@14@@oe@26-8-2013
1000008200350@unknown@formal@none@1@S@An example of a word-based translation system is the freely available ⌊>GIZA++>⌋ package (⌊>GPL>⌋ed), which includes ⌊>IBM>⌋ models.@@@@1@18@@oe@26-8-2013
1000008200360@unknown@formal@none@1@S@⌊=Phrase-based translation¦2=⌋@@@@1@2@@oe@26-8-2013
1000008200370@unknown@formal@none@1@S@In phrase-based translation, the restrictions produced by word-based translation have been tried to reduce by translating sequences of words to sequences of words, where the lengths can differ.@@@@1@28@@oe@26-8-2013
1000008200380@unknown@formal@none@1@S@The sequences of words are called, for instance, blocks or phrases, but typically are not linguistic ⌊>phrase>⌋s but phrases found using statistical methods from the corpus.@@@@1@26@@oe@26-8-2013
1000008200390@unknown@formal@none@1@S@Restricting the phrases to linguistic phrases has been shown to decrease translation quality.@@@@1@13@@oe@26-8-2013
1000008200400@unknown@formal@none@1@S@⌊=Challenges with statistical machine translation¦2=⌋@@@@1@5@@oe@26-8-2013
1000008200410@unknown@formal@none@1@S@Problems that statistical machine translation have to deal with include@@@@1@10@@oe@26-8-2013
1000008200420@unknown@formal@none@1@S@⌊=Different word orders¦3=⌋@@@@1@3@@oe@26-8-2013
1000008200430@unknown@formal@none@1@S@Word order in languages differ.@@@@1@5@@oe@26-8-2013
1000008200440@unknown@formal@none@1@S@Some classification can be done by naming the typical order of subject (S), verb (V) and object (O) in a sentence and one can talk, for instance, of SVO or VSO languages.@@@@1@32@@oe@26-8-2013
1000008200450@unknown@formal@none@1@S@There are also additional differences in word orders, for instance, where modifiers for nouns are located.@@@@1@16@@oe@26-8-2013
1000008200460@unknown@formal@none@1@S@In ⌊>Speech Recognition>⌋, the speech signal and the corresponding textual representation can be mapped to each other in blocks in order.@@@@1@21@@oe@26-8-2013
1000008200470@unknown@formal@none@1@S@This is not always the case with the same text in two languages.@@@@1@13@@oe@26-8-2013
1000008200480@unknown@formal@none@1@S@For SMT, the translation model is only able to translate small sequences of words and word order has to be taken into account somehow.@@@@1@24@@oe@26-8-2013
1000008200490@unknown@formal@none@1@S@Typical solution has been re-ordering models, where a distribution of location changes for each item of translation is approximated from aligned bi-text.@@@@1@22@@oe@26-8-2013
1000008200500@unknown@formal@none@1@S@Different location changes can be ranked with the help of the language model and the best can be selected.@@@@1@19@@oe@26-8-2013
1000008200510@unknown@formal@none@1@S@⌊=Out of vocabulary (OOV) words¦3=⌋@@@@1@5@@oe@26-8-2013
1000008200520@unknown@formal@none@1@S@SMT systems store different word forms as separate symbols without any relation to each other and word forms or phrases that were not in the training data cannot be translated.@@@@1@30@@oe@26-8-2013
1000008200530@unknown@formal@none@1@S@Main reasons for out of vocabulary words are the limitation of training data, domain changes and morphology.@@@@1@17@@oe@26-8-2013
1000008300010@unknown@formal@none@1@S@⌊δStatisticsδ⌋@@@@1@1@@oe@26-8-2013
1000008300020@unknown@formal@none@1@S@⌊∗Statistics∗⌋ is a ⌊>mathematical science>⌋ pertaining to the collection, analysis, interpretation or explanation, and presentation of ⌊>data>⌋.@@@@1@17@@oe@26-8-2013
1000008300030@unknown@formal@none@1@S@It is applicable to a wide variety of ⌊>academic discipline>⌋s, from the ⌊>natural>⌋ and ⌊>social science>⌋s to the ⌊>humanities>⌋, government and business.@@@@1@22@@oe@26-8-2013
1000008300040@unknown@formal@none@1@S@Statistical methods can be used to summarize or describe a collection of data; this is called ⌊∗⌊>descriptive statistics>⌋∗⌋.@@@@1@18@@oe@26-8-2013
1000008300050@unknown@formal@none@1@S@In addition, patterns in the data may be ⌊>modeled>⌋ in a way that accounts for ⌊>random>⌋ness and uncertainty in the observations, and then used to draw inferences about the process or population being studied; this is called ⌊∗⌊>inferential statistics>⌋∗⌋.@@@@1@39@@oe@26-8-2013
1000008300060@unknown@formal@none@1@S@Both descriptive and inferential statistics comprise ⌊∗applied statistics∗⌋.@@@@1@8@@oe@26-8-2013
1000008300070@unknown@formal@none@1@S@There is also a discipline called ⌊∗⌊>mathematical statistics>⌋∗⌋, which is concerned with the theoretical basis of the subject.@@@@1@18@@oe@26-8-2013
1000008300080@unknown@formal@none@1@S@The word ⌊∗⌊/statistics/⌋∗⌋ is also the plural of ⌊∗⌊/⌊>statistic>⌋/⌋∗⌋ (singular), which refers to the result of applying a statistical algorithm to a set of data, as in ⌊>economic statistics>⌋, ⌊>crime statistics>⌋, etc.@@@@1@32@@oe@26-8-2013
1000008300090@unknown@formal@none@1@S@⌊=History¦2=⌋@@@@1@1@@oe@26-8-2013
1000008300100@unknown@formal@none@1@S@⌊/"Five men, ⌊>Conring>⌋,⌊> Achenwall>⌋, ⌊>Süssmilch>⌋, ⌊>Graunt>⌋ and ⌊>Petty>⌋ have been honored by different writers as the founder of statistics."/⌋ claims one source (Willcox, Walter (1938) ⌊/The Founder of Statistics/⌋.@@@@1@29@@oe@26-8-2013
1000008300110@unknown@formal@none@1@S@Review of the ⌊>International Statistical Institute>⌋ 5(4):321-328.)@@@@1@7@@oe@26-8-2013
1000008300120@unknown@formal@none@1@S@Some scholars pinpoint the origin of statistics to 1662, with the publication of "⌊>Observations on the Bills of Mortality>⌋" by John Graunt.@@@@1@22@@oe@26-8-2013
1000008300130@unknown@formal@none@1@S@Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data.@@@@1@19@@oe@26-8-2013
1000008300140@unknown@formal@none@1@S@The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general.@@@@1@23@@oe@26-8-2013
1000008300150@unknown@formal@none@1@S@Today, statistics is widely employed in government, business, and the natural and social sciences.@@@@1@14@@oe@26-8-2013
1000008300160@unknown@formal@none@1@S@Because of its empirical roots and its applications, statistics is generally considered not to be a subfield of pure mathematics, but rather a distinct branch of applied mathematics.@@@@1@28@@oe@26-8-2013
1000008300170@unknown@formal@none@1@S@Its mathematical foundations were laid in the 17th century with the development of ⌊>probability theory>⌋ by ⌊>Pascal>⌋ and ⌊>Fermat>⌋.@@@@1@19@@oe@26-8-2013
1000008300180@unknown@formal@none@1@S@Probability theory arose from the study of games of chance.@@@@1@10@@oe@26-8-2013
1000008300190@unknown@formal@none@1@S@The ⌊>method of least squares>⌋ was first described by ⌊>Carl Friedrich Gauss>⌋ around 1794.@@@@1@14@@oe@26-8-2013
1000008300200@unknown@formal@none@1@S@The use of modern ⌊>computer>⌋s has expedited large-scale statistical computation, and has also made possible new methods that are impractical to perform manually.@@@@1@23@@oe@26-8-2013
1000008300210@unknown@formal@none@1@S@⌊=Overview¦2=⌋@@@@1@1@@oe@26-8-2013
1000008300220@unknown@formal@none@1@S@In applying statistics to a scientific, industrial, or societal problem, one begins with a process or ⌊>population>⌋ to be studied.@@@@1@20@@oe@26-8-2013
1000008300230@unknown@formal@none@1@S@This might be a population of people in a country, of crystal grains in a rock, or of goods manufactured by a particular factory during a given period.@@@@1@28@@oe@26-8-2013
1000008300240@unknown@formal@none@1@S@It may instead be a process observed at various times; data collected about this kind of "population" constitute what is called a ⌊>time series>⌋.@@@@1@24@@oe@26-8-2013
1000008300250@unknown@formal@none@1@S@For practical reasons, rather than compiling data about an entire population, one usually studies a chosen subset of the population, called a ⌊>sample>⌋.@@@@1@23@@oe@26-8-2013
1000008300260@unknown@formal@none@1@S@Data are collected about the sample in an observational or ⌊>experiment>⌋al setting.@@@@1@12@@oe@26-8-2013
1000008300270@unknown@formal@none@1@S@The data are then subjected to statistical analysis, which serves two related purposes: description and inference.@@@@1@16@@oe@26-8-2013
1000008300280@unknown@formal@none@1@S@⌊•⌊#⌊>Descriptive statistics>⌋ can be used to summarize the data, either numerically or graphically, to describe the sample.@@@@1@17@@oe@26-8-2013
1000008300290@unknown@formal@none@1@S@Basic examples of numerical descriptors include the ⌊>mean>⌋ and ⌊>standard deviation>⌋.@@@@1@11@@oe@26-8-2013
1000008300300@unknown@formal@none@1@S@Graphical summarizations include various kinds of charts and graphs.#⌋@@@@1@9@@oe@26-8-2013
1000008300310@unknown@formal@none@1@S@⌊#⌊>Inferential statistics>⌋ is used to model patterns in the data, accounting for randomness and drawing inferences about the larger population.@@@@1@20@@oe@26-8-2013
1000008300320@unknown@formal@none@1@S@These inferences may take the form of answers to yes/no questions (⌊>hypothesis testing>⌋), estimates of numerical characteristics (⌊>estimation>⌋), descriptions of association (⌊>correlation>⌋), or modeling of relationships (⌊>regression>⌋).@@@@1@27@@oe@26-8-2013
1000008300330@unknown@formal@none@1@S@Other ⌊>modeling>⌋ techniques include ⌊>ANOVA>⌋, ⌊>time series>⌋, and ⌊>data mining>⌋.#⌋•⌋@@@@1@10@@oe@26-8-2013
1000008300340@unknown@formal@none@1@S@The concept of correlation is particularly noteworthy.@@@@1@7@@oe@26-8-2013
1000008300350@unknown@formal@none@1@S@Statistical analysis of a ⌊>data set>⌋ may reveal that two variables (that is, two properties of the population under consideration) tend to vary together, as if they are connected.@@@@1@29@@oe@26-8-2013
1000008300360@unknown@formal@none@1@S@For example, a study of annual income and age of death among people might find that poor people tend to have shorter lives than affluent people.@@@@1@26@@oe@26-8-2013
1000008300370@unknown@formal@none@1@S@The two variables are said to be correlated (which is a positive correlation in this case).@@@@1@16@@oe@26-8-2013
1000008300380@unknown@formal@none@1@S@However, one cannot immediately infer the existence of a causal relationship between the two variables.@@@@1@15@@oe@26-8-2013
1000008300390@unknown@formal@none@1@S@(See ⌊>Correlation does not imply causation>⌋.)@@@@1@6@@oe@26-8-2013
1000008300400@unknown@formal@none@1@S@The correlated phenomena could be caused by a third, previously unconsidered phenomenon, called a ⌊>lurking variable>⌋ or ⌊>confounding variable>⌋.@@@@1@19@@oe@26-8-2013
1000008300410@unknown@formal@none@1@S@If the sample is representative of the population, then inferences and conclusions made from the sample can be extended to the population as a whole.@@@@1@25@@oe@26-8-2013
1000008300420@unknown@formal@none@1@S@A major problem lies in determining the extent to which the chosen sample is representative.@@@@1@15@@oe@26-8-2013
1000008300430@unknown@formal@none@1@S@Statistics offers methods to estimate and correct for randomness in the sample and in the data collection procedure, as well as methods for designing robust experiments in the first place.@@@@1@30@@oe@26-8-2013
1000008300440@unknown@formal@none@1@S@(See ⌊>experimental design>⌋.)@@@@1@3@@oe@26-8-2013
1000008300450@unknown@formal@none@1@S@The fundamental mathematical concept employed in understanding such randomness is ⌊>probability>⌋.@@@@1@11@@oe@26-8-2013
1000008300460@unknown@formal@none@1@S@⌊>Mathematical statistics>⌋ (also called ⌊>statistical theory>⌋) is the branch of ⌊>applied mathematics>⌋ that uses probability theory and ⌊>analysis>⌋ to examine the theoretical basis of statistics.@@@@1@25@@oe@26-8-2013
1000008300470@unknown@formal@none@1@S@The use of any statistical method is valid only when the system or population under consideration satisfies the basic mathematical assumptions of the method.@@@@1@24@@oe@26-8-2013
1000008300480@unknown@formal@none@1@S@⌊>Misuse of statistics>⌋ can produce subtle but serious errors in description and interpretation — subtle in the sense that even experienced professionals sometimes make such errors, serious in the sense that they may affect, for instance, social policy, medical practice and the reliability of structures such as bridges.@@@@1@48@@oe@26-8-2013
1000008300490@unknown@formal@none@1@S@Even when statistics is correctly applied, the results can be difficult for the non-expert to interpret.@@@@1@16@@oe@26-8-2013
1000008300500@unknown@formal@none@1@S@For example, the ⌊>statistical significance>⌋ of a trend in the data, which measures the extent to which the trend could be caused by random variation in the sample, may not agree with one's intuitive sense of its significance.@@@@1@38@@oe@26-8-2013
1000008300510@unknown@formal@none@1@S@The set of basic statistical skills (and skepticism) needed by people to deal with information in their everyday lives is referred to as ⌊>statistical literacy>⌋.@@@@1@25@@oe@26-8-2013
1000008300520@unknown@formal@none@1@S@⌊=Statistical methods¦2=⌋@@@@1@2@@oe@26-8-2013
1000008300530@unknown@formal@none@1@S@⌊=Experimental and observational studies¦3=⌋@@@@1@4@@oe@26-8-2013
1000008300540@unknown@formal@none@1@S@A common goal for a statistical research project is to investigate ⌊>causality>⌋, and in particular to draw a conclusion on the effect of changes in the values of predictors or ⌊>independent variable>⌋s on response or ⌊>dependent variable>⌋s.@@@@1@37@@oe@26-8-2013
1000008300550@unknown@formal@none@1@S@There are two major types of causal statistical studies, experimental studies and observational studies.@@@@1@14@@oe@26-8-2013
1000008300560@unknown@formal@none@1@S@In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed.@@@@1@24@@oe@26-8-2013
1000008300570@unknown@formal@none@1@S@The difference between the two types lies in how the study is actually conducted.@@@@1@14@@oe@26-8-2013
1000008300580@unknown@formal@none@1@S@Each can be very effective.@@@@1@5@@oe@26-8-2013
1000008300590@unknown@formal@none@1@S@An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements.@@@@1@35@@oe@26-8-2013
1000008300600@unknown@formal@none@1@S@In contrast, an observational study does not involve experimental manipulation.@@@@1@10@@oe@26-8-2013
1000008300610@unknown@formal@none@1@S@Instead, data are gathered and correlations between predictors and response are investigated.@@@@1@12@@oe@26-8-2013
1000008300620@unknown@formal@none@1@S@An example of an experimental study is the famous ⌊>Hawthorne studies>⌋, which attempted to test the changes to the working environment at the Hawthorne plant of the Western Electric Company.@@@@1@30@@oe@26-8-2013
1000008300630@unknown@formal@none@1@S@The researchers were interested in determining whether increased illumination would increase the productivity of the ⌊>assembly line>⌋ workers.@@@@1@18@@oe@26-8-2013
1000008300640@unknown@formal@none@1@S@The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected the productivity.@@@@1@29@@oe@26-8-2013
1000008300650@unknown@formal@none@1@S@It turned out that the productivity indeed improved (under the experimental conditions).@@@@1@12@@oe@26-8-2013
1000008300660@unknown@formal@none@1@S@(See ⌊>Hawthorne effect>⌋.)@@@@1@3@@oe@26-8-2013
1000008300670@unknown@formal@none@1@S@However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a ⌊>control group>⌋ and ⌊>blindedness>⌋.@@@@1@22@@oe@26-8-2013
1000008300680@unknown@formal@none@1@S@An example of an observational study is a study which explores the correlation between smoking and lung cancer.@@@@1@18@@oe@26-8-2013
1000008300690@unknown@formal@none@1@S@This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis.@@@@1@21@@oe@26-8-2013
1000008300700@unknown@formal@none@1@S@In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a ⌊>case-control study>⌋, and then look for the number of cases of lung cancer in each group.@@@@1@32@@oe@26-8-2013
1000008300710@unknown@formal@none@1@S@The basic steps of an experiment are;@@@@1@7@@oe@26-8-2013
1000008300720@unknown@formal@none@1@S@⌊•⌊#Planning the research, including determining information sources, research subject selection, and ⌊>ethical>⌋ considerations for the proposed research and method.#⌋@@@@1@19@@oe@26-8-2013
1000008300730@unknown@formal@none@1@S@⌊#⌊>Design of experiments>⌋, concentrating on the system model and the interaction of independent and dependent variables.#⌋@@@@1@16@@oe@26-8-2013
1000008300740@unknown@formal@none@1@S@⌊#⌊>Summarizing a collection of observations>⌋ to feature their commonality by suppressing details.@@@@1@12@@oe@26-8-2013
1000008300750@unknown@formal@none@1@S@(⌊>Descriptive statistics>⌋)#⌋@@@@1@2@@oe@26-8-2013
1000008300760@unknown@formal@none@1@S@⌊#Reaching consensus about what ⌊>the observations tell>⌋ about the world being observed.@@@@1@12@@oe@26-8-2013
1000008300770@unknown@formal@none@1@S@(⌊>Statistical inference>⌋)#⌋@@@@1@2@@oe@26-8-2013
1000008300780@unknown@formal@none@1@S@⌊#Documenting / presenting the results of the study.#⌋•⌋@@@@1@8@@oe@26-8-2013
1000008300790@unknown@formal@none@1@S@⌊=Levels of measurement¦3=⌋@@@@1@3@@oe@26-8-2013
1000008300800@unknown@formal@none@1@S@⌊⇥⌊/See: ⌊>Stanley Stevens' "Scales of measurement" (1946): nominal, ordinal, interval, ratio>⌋/⌋⇥⌋@@@@1@11@@oe@26-8-2013
1000008300810@unknown@formal@none@1@S@There are four types of measurements or ⌊>levels of measurement>⌋ or measurement scales used in statistics: nominal, ordinal, interval, and ratio.@@@@1@21@@oe@26-8-2013
1000008300820@unknown@formal@none@1@S@They have different degrees of usefulness in statistical ⌊>research>⌋.@@@@1@9@@oe@26-8-2013
1000008300830@unknown@formal@none@1@S@Ratio measurements have both a zero value defined and the distances between different measurements defined; they provide the greatest flexibility in statistical methods that can be used for analyzing the data.@@@@1@31@@oe@26-8-2013
1000008300840@unknown@formal@none@1@S@Interval measurements have meaningful distances between measurements defined, but have no meaningful zero value defined (as in the case with IQ measurements or with temperature measurements in ⌊>Fahrenheit>⌋).@@@@1@28@@oe@26-8-2013
1000008300850@unknown@formal@none@1@S@Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values.@@@@1@16@@oe@26-8-2013
1000008300860@unknown@formal@none@1@S@Nominal measurements have no meaningful rank order among values.@@@@1@9@@oe@26-8-2013
1000008300870@unknown@formal@none@1@S@Since variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are called together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative or ⌊>continuous variables>⌋ due to their numerical nature.@@@@1@40@@oe@26-8-2013
1000008300880@unknown@formal@none@1@S@⌊=Statistical techniques¦3=⌋@@@@1@2@@oe@26-8-2013
1000008300890@unknown@formal@none@1@S@Some well known statistical ⌊>test>⌋s and ⌊>procedure>⌋s for ⌊>research>⌋ ⌊>observation>⌋s are:@@@@1@11@@oe@26-8-2013
1000008300900@unknown@formal@none@1@S@⌊•⌊#⌊>Student's t-test>⌋@@@@1@2@@oe@26-8-2013
1000008300910@unknown@formal@none@1@S@⌊>chi-square test>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008300920@unknown@formal@none@1@S@⌊#⌊>Analysis of variance>⌋ (ANOVA)#⌋@@@@1@4@@oe@26-8-2013
1000008300930@unknown@formal@none@1@S@⌊#⌊>Mann-Whitney U>⌋@@@@1@2@@oe@26-8-2013
1000008300940@unknown@formal@none@1@S@⌊>Regression analysis>⌋@@@@1@2@@oe@26-8-2013
1000008300950@unknown@formal@none@1@S@⌊>Factor Analysis>⌋@@@@1@2@@oe@26-8-2013
1000008300960@unknown@formal@none@1@S@⌊>Correlation>⌋@@@@1@1@@oe@26-8-2013
1000008300970@unknown@formal@none@1@S@⌊>Pearson product-moment correlation coefficient>⌋@@@@1@4@@oe@26-8-2013
1000008300980@unknown@formal@none@1@S@⌊>Spearman's rank correlation coefficient>⌋@@@@1@4@@oe@26-8-2013
1000008300990@unknown@formal@none@1@S@⌊>Time Series Analysis>⌋#⌋•⌋@@@@1@3@@oe@26-8-2013
1000008301000@unknown@formal@none@1@S@⌊=Specialized disciplines¦2=⌋@@@@1@2@@oe@26-8-2013
1000008301010@unknown@formal@none@1@S@Some fields of inquiry use applied statistics so extensively that they have ⌊>specialized terminology>⌋.@@@@1@14@@oe@26-8-2013
1000008301020@unknown@formal@none@1@S@These disciplines include:@@@@1@3@@oe@26-8-2013
1000008301030@unknown@formal@none@1@S@⌊•⌊#⌊>Actuarial science>⌋@@@@1@2@@oe@26-8-2013
1000008301040@unknown@formal@none@1@S@⌊>Applied information economics>⌋@@@@1@3@@oe@26-8-2013
1000008301050@unknown@formal@none@1@S@⌊>Biostatistics>⌋#⌋@@@@1@1@@oe@26-8-2013
1000008301060@unknown@formal@none@1@S@⌊#⌊>Bootstrap>⌋ & ⌊>Jackknife Resampling>⌋#⌋@@@@1@4@@oe@26-8-2013
1000008301070@unknown@formal@none@1@S@⌊#⌊>Business statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301080@unknown@formal@none@1@S@⌊>Data analysis>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008301090@unknown@formal@none@1@S@⌊#⌊>Data mining>⌋ (applying statistics and ⌊>pattern recognition>⌋ to discover knowledge from data)#⌋@@@@1@12@@oe@26-8-2013
1000008301100@unknown@formal@none@1@S@⌊#⌊>Demography>⌋#⌋@@@@1@1@@oe@26-8-2013
1000008301110@unknown@formal@none@1@S@⌊#⌊>Economic statistics>⌋ (Econometrics)#⌋@@@@1@3@@oe@26-8-2013
1000008301120@unknown@formal@none@1@S@⌊#⌊>Energy statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301130@unknown@formal@none@1@S@⌊>Engineering statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301140@unknown@formal@none@1@S@⌊>Environmental Statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301150@unknown@formal@none@1@S@⌊>Epidemiology>⌋#⌋@@@@1@1@@oe@26-8-2013
1000008301160@unknown@formal@none@1@S@⌊#⌊>Geography>⌋ and ⌊>Geographic Information Systems>⌋, more specifically in ⌊>Spatial analysis>⌋#⌋@@@@1@10@@oe@26-8-2013
1000008301170@unknown@formal@none@1@S@⌊#⌊>Image processing>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008301180@unknown@formal@none@1@S@⌊#⌊>Multivariate Analysis>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008301190@unknown@formal@none@1@S@⌊#⌊>Psychological statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301200@unknown@formal@none@1@S@⌊>Quality>⌋@@@@1@1@@oe@26-8-2013
1000008301210@unknown@formal@none@1@S@⌊>Social statistics>⌋@@@@1@2@@oe@26-8-2013
1000008301220@unknown@formal@none@1@S@⌊>Statistical literacy>⌋@@@@1@2@@oe@26-8-2013
1000008301230@unknown@formal@none@1@S@⌊>Statistical modeling>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008301240@unknown@formal@none@1@S@⌊#⌊>Statistical survey>⌋s#⌋@@@@1@2@@oe@26-8-2013
1000008301250@unknown@formal@none@1@S@⌊#Process analysis and ⌊>chemometrics>⌋ (for analysis of data from ⌊>analytical chemistry>⌋ and ⌊>chemical engineering>⌋)#⌋@@@@1@14@@oe@26-8-2013
1000008301260@unknown@formal@none@1@S@⌊#⌊>Structured data analysis (statistics)>⌋@@@@1@4@@oe@26-8-2013
1000008301270@unknown@formal@none@1@S@⌊>Survival analysis>⌋@@@@1@2@@oe@26-8-2013
1000008301280@unknown@formal@none@1@S@⌊>Reliability engineering>⌋#⌋•⌋@@@@1@2@@oe@26-8-2013
1000008301290@unknown@formal@none@1@S@⌊•⌊#Statistics in various sports, particularly ⌊>baseball>⌋ and ⌊>cricket>⌋#⌋•⌋@@@@1@8@@oe@26-8-2013
1000008301300@unknown@formal@none@1@S@Statistics form a key basis tool in business and manufacturing as well.@@@@1@12@@oe@26-8-2013
1000008301310@unknown@formal@none@1@S@It is used to understand measurement systems variability, control processes (as in ⌊>statistical process control>⌋ or SPC), for summarizing data, and to make data-driven decisions.@@@@1@25@@oe@26-8-2013
1000008301320@unknown@formal@none@1@S@In these roles, it is a key tool, and perhaps the only reliable tool.@@@@1@14@@oe@26-8-2013
1000008301330@unknown@formal@none@1@S@⌊=Statistical computing¦2=⌋@@@@1@2@@oe@26-8-2013
1000008301340@unknown@formal@none@1@S@The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science.@@@@1@28@@oe@26-8-2013
1000008301350@unknown@formal@none@1@S@Early statistical models were almost always from the class of ⌊>linear model>⌋s, but powerful computers, coupled with suitable numerical ⌊>algorithms>⌋, caused an increased interest in ⌊>nonlinear models>⌋ (especially ⌊>neural networks>⌋ and ⌊>decision tree>⌋s) as well as the creation of new types, such as ⌊>generalised linear model>⌋s and ⌊>multilevel model>⌋s.@@@@1@49@@oe@26-8-2013
1000008301360@unknown@formal@none@1@S@Increased computing power has also led to the growing popularity of computationally-intensive methods based on ⌊>resampling>⌋, such as permutation tests and the ⌊>bootstrap>⌋, while techniques such as ⌊>Gibbs sampling>⌋ have made Bayesian methods more feasible.@@@@1@35@@oe@26-8-2013
1000008301370@unknown@formal@none@1@S@The computer revolution has implications for the future of statistics with new emphasis on "experimental" and "empirical" statistics.@@@@1@18@@oe@26-8-2013
1000008301380@unknown@formal@none@1@S@A large number of both general and special purpose ⌊>statistical software>⌋ are now available.@@@@1@14@@oe@26-8-2013
1000008301390@unknown@formal@none@1@S@⌊=Misuse¦2=⌋@@@@1@1@@oe@26-8-2013
1000008301400@unknown@formal@none@1@S@There is a general perception that statistical knowledge is all-too-frequently intentionally ⌊>misused>⌋ by finding ways to interpret only the data that are favorable to the presenter.@@@@1@26@@oe@26-8-2013
1000008301410@unknown@formal@none@1@S@A famous saying attributed to ⌊>Benjamin Disraeli>⌋ is, "⌊>There are three kinds of lies: lies, damned lies, and statistics>⌋"; and Harvard President ⌊>Lawrence Lowell>⌋ wrote in 1909 that statistics, ⌊/"like veal pies, are good if you know the person that made them, and are sure of the ingredients"/⌋.@@@@1@48@@oe@26-8-2013
1000008301420@unknown@formal@none@1@S@If various studies appear to contradict one another, then the public may come to distrust such studies.@@@@1@17@@oe@26-8-2013
1000008301430@unknown@formal@none@1@S@For example, one study may suggest that a given diet or activity raises ⌊>blood pressure>⌋, while another may suggest that it lowers blood pressure.@@@@1@24@@oe@26-8-2013
1000008301440@unknown@formal@none@1@S@The discrepancy can arise from subtle variations in experimental design, such as differences in the patient groups or research protocols, that are not easily understood by the non-expert.@@@@1@28@@oe@26-8-2013
1000008301450@unknown@formal@none@1@S@(Media reports sometimes omit this vital contextual information entirely.)@@@@1@9@@oe@26-8-2013
1000008301460@unknown@formal@none@1@S@By choosing (or rejecting, or modifying) a certain sample, results can be manipulated.@@@@1@13@@oe@26-8-2013
1000008301470@unknown@formal@none@1@S@Such manipulations need not be malicious or devious; they can arise from unintentional biases of the researcher.@@@@1@17@@oe@26-8-2013
1000008301480@unknown@formal@none@1@S@The graphs used to summarize data can also be misleading.@@@@1@10@@oe@26-8-2013
1000008301490@unknown@formal@none@1@S@Deeper criticisms come from the fact that the hypothesis testing approach, widely used and in many cases required by law or regulation, forces one hypothesis (the ⌊>null hypothesis>⌋) to be "favored", and can also seem to exaggerate the importance of minor differences in large studies.@@@@1@45@@oe@26-8-2013
1000008301500@unknown@formal@none@1@S@A difference that is highly statistically significant can still be of no practical significance.@@@@1@14@@oe@26-8-2013
1000008301510@unknown@formal@none@1@S@(See ⌊>criticism of hypothesis testing>⌋ and ⌊>controversy over the null hypothesis>⌋.)@@@@1@11@@oe@26-8-2013
1000008301520@unknown@formal@none@1@S@One response is by giving a greater emphasis on the ⌊>⌊/p/⌋-value>⌋ than simply reporting whether a hypothesis is rejected at the given level of significance.@@@@1@25@@oe@26-8-2013
1000008301530@unknown@formal@none@1@S@The ⌊/p/⌋-value, however, does not indicate the size of the effect.@@@@1@11@@oe@26-8-2013
1000008301540@unknown@formal@none@1@S@Another increasingly common approach is to report ⌊>confidence interval>⌋s.@@@@1@9@@oe@26-8-2013
1000008301550@unknown@formal@none@1@S@Although these are produced from the same calculations as those of hypothesis tests or ⌊/p/⌋-values, they describe both the size of the effect and the uncertainty surrounding it.@@@@1@28@@oe@26-8-2013
1000008400010@unknown@formal@none@1@S@⌊δSyntaxδ⌋@@@@1@1@@oe@26-8-2013
1000008400020@unknown@formal@none@1@S@In ⌊>linguistics>⌋, ⌊∗syntax∗⌋ (from ⌊>Ancient Greek>⌋ ⌊λσυν-¦grc¦συν-¦Langλ⌋ ⌊/syn-/⌋, "together", and ⌊λτάξις¦grc¦τάξις¦Langλ⌋ ⌊/táxis/⌋, "arrangement") is the study of the principles and rules for constructing ⌊>sentence>⌋s in ⌊>natural language>⌋s.@@@@1@27@@oe@26-8-2013
1000008400030@unknown@formal@none@1@S@In addition to referring to the discipline, the term ⌊/syntax/⌋ is also used to refer directly to the rules and principles that govern the sentence structure of any individual language, as in "the ⌊>syntax of Modern Irish>⌋".@@@@1@37@@oe@26-8-2013
1000008400040@unknown@formal@none@1@S@Modern research in syntax attempts to ⌊>describe languages>⌋ in terms of such rules.@@@@1@13@@oe@26-8-2013
1000008400050@unknown@formal@none@1@S@Many professionals in this discipline attempt to find ⌊>general rules>⌋ that apply to all natural languages.@@@@1@16@@oe@26-8-2013
1000008400060@unknown@formal@none@1@S@The term ⌊/syntax/⌋ is also sometimes used to refer to the rules governing the behavior of mathematical systems, such as ⌊>logic>⌋, artificial formal languages, and computer programming languages.@@@@1@28@@oe@26-8-2013
1000008400070@unknown@formal@none@1@S@⌊=Early history¦2=⌋@@@@1@2@@oe@26-8-2013
1000008400080@unknown@formal@none@1@S@Works on grammar were being written long before modern syntax came about; the ⌊/Aṣṭādhyāyī/⌋ of ⌊>Pāṇini>⌋ is often cited as an example of a pre-modern work that approaches the sophistication of a modern syntactic theory.@@@@1@35@@oe@26-8-2013
1000008400090@unknown@formal@none@1@S@In the West, the school of thought that came to be known as "traditional grammar" began with the work of ⌊>Dionysius Thrax>⌋.@@@@1@22@@oe@26-8-2013
1000008400100@unknown@formal@none@1@S@For centuries, work in syntax was dominated by a framework known as ⌊λ⌊/grammaire générale/⌋¦fr¦⌊/grammaire générale/⌋¦Langλ⌋, first expounded in 1660 by ⌊>Antoine Arnauld>⌋ in a book of the same title.@@@@1@29@@oe@26-8-2013
1000008400110@unknown@formal@none@1@S@This system took as its basic premise the assumption that language is a direct reflection of thought processes and therefore there is a single, most natural way to express a thought.@@@@1@31@@oe@26-8-2013
1000008400120@unknown@formal@none@1@S@That way, coincidentally, was exactly the way it was expressed in French.@@@@1@12@@oe@26-8-2013
1000008400130@unknown@formal@none@1@S@However, in the 19th century, with the development of ⌊>historical-comparative linguistics>⌋, linguists began to realize the sheer diversity of human language, and to question fundamental assumptions about the relationship between language and logic.@@@@1@33@@oe@26-8-2013
1000008400140@unknown@formal@none@1@S@It became apparent that there was no such thing as a most natural way to express a thought, and therefore logic could no longer be relied upon as a basis for studying the structure of language.@@@@1@36@@oe@26-8-2013
1000008400150@unknown@formal@none@1@S@The Port-Royal grammar modeled the study of syntax upon that of logic (indeed, large parts of the ⌊>Port-Royal Logic>⌋ were copied or adapted from the ⌊/Grammaire générale/⌋).@@@@1@27@@oe@26-8-2013
1000008400160@unknown@formal@none@1@S@Syntactic categories were identified with logical ones, and all sentences were analyzed in terms of "Subject – Copula – Predicate".@@@@1@20@@oe@26-8-2013
1000008400170@unknown@formal@none@1@S@Initially, this view was adopted even by the early comparative linguists such as ⌊>Franz Bopp>⌋.@@@@1@15@@oe@26-8-2013
1000008400180@unknown@formal@none@1@S@The central role of syntax within theoretical linguistics became clear only in the 20th century, which could reasonably be called the "century of syntactic theory" as far as linguistics is concerned.@@@@1@31@@oe@26-8-2013
1000008400190@unknown@formal@none@1@S@For a detailed and critical survey of the history of syntax in the last two centuries, see the monumental work by Graffi (2001).@@@@1@23@@oe@26-8-2013
1000008400200@unknown@formal@none@1@S@⌊=Modern theories¦2=⌋@@@@1@2@@oe@26-8-2013
1000008400210@unknown@formal@none@1@S@There are a number of theoretical approaches to the discipline of syntax.@@@@1@12@@oe@26-8-2013
1000008400220@unknown@formal@none@1@S@Many linguists (e.g. ⌊>Noam Chomsky>⌋) see syntax as a branch of biology, since they conceive of syntax as the study of linguistic knowledge as embodied in the human ⌊>mind>⌋.@@@@1@29@@oe@26-8-2013
1000008400230@unknown@formal@none@1@S@Others (e.g. ⌊>Gerald Gazdar>⌋) take a more ⌊>Platonistic>⌋ view, since they regard syntax to be the study of an abstract ⌊>formal system>⌋.@@@@1@22@@oe@26-8-2013
1000008400240@unknown@formal@none@1@S@Yet others (e.g. ⌊>Joseph Greenberg>⌋) consider grammar a taxonomical device to reach broad generalizations across languages.@@@@1@16@@oe@26-8-2013
1000008400250@unknown@formal@none@1@S@Some of the major approaches to the discipline are listed below.@@@@1@11@@oe@26-8-2013
1000008400260@unknown@formal@none@1@S@⌊=Generative grammar¦3=⌋@@@@1@2@@oe@26-8-2013
1000008400270@unknown@formal@none@1@S@The hypothesis of ⌊>generative grammar>⌋ is that language is a structure of the human mind.@@@@1@15@@oe@26-8-2013
1000008400280@unknown@formal@none@1@S@The goal of generative grammar is to make a complete model of this inner language (known as ⌊/⌊>i-language>⌋/⌋).@@@@1@18@@oe@26-8-2013
1000008400290@unknown@formal@none@1@S@This model could be used to describe all human language and to predict the ⌊>grammaticality>⌋ of any given utterance (that is, to predict whether the utterance would sound correct to native speakers of the language).@@@@1@35@@oe@26-8-2013
1000008400300@unknown@formal@none@1@S@This approach to language was pioneered by ⌊>Noam Chomsky>⌋.@@@@1@9@@oe@26-8-2013
1000008400310@unknown@formal@none@1@S@Most generative theories (although not all of them) assume that syntax is based upon the constituent structure of sentences.@@@@1@19@@oe@26-8-2013
1000008400320@unknown@formal@none@1@S@Generative grammars are among the theories that focus primarily on the form of a sentence, rather than its communicative function.@@@@1@20@@oe@26-8-2013
1000008400330@unknown@formal@none@1@S@Among the many generative theories of linguistics are:@@@@1@8@@oe@26-8-2013
1000008400340@unknown@formal@none@1@S@⌊•⌊#⌊>Transformational Grammar>⌋ (TG) (now largely out of date)#⌋@@@@1@8@@oe@26-8-2013
1000008400350@unknown@formal@none@1@S@⌊#⌊>Government and binding theory>⌋ (GB) (common in the late 1970s and 1980s)#⌋@@@@1@12@@oe@26-8-2013
1000008400360@unknown@formal@none@1@S@⌊#⌊>Minimalism>⌋ (MP) (the most recent Chomskyan version of generative grammar)#⌋•⌋@@@@1@10@@oe@26-8-2013
1000008400370@unknown@formal@none@1@S@Other theories that find their origin in the generative paradigm are:@@@@1@11@@oe@26-8-2013
1000008400380@unknown@formal@none@1@S@⌊•⌊#⌊>Generative semantics>⌋ (now largely out of date)#⌋@@@@1@7@@oe@26-8-2013
1000008400390@unknown@formal@none@1@S@⌊#⌊>Relational grammar>⌋ (RG) (now largely out of date)#⌋@@@@1@8@@oe@26-8-2013
1000008400400@unknown@formal@none@1@S@⌊#⌊>Arc Pair grammar>⌋#⌋@@@@1@3@@oe@26-8-2013
1000008400410@unknown@formal@none@1@S@⌊#⌊>Generalized phrase structure grammar>⌋ (GPSG; now largely out of date)#⌋@@@@1@10@@oe@26-8-2013
1000008400420@unknown@formal@none@1@S@⌊#⌊>Head-driven phrase structure grammar>⌋ (HPSG)#⌋@@@@1@5@@oe@26-8-2013
1000008400430@unknown@formal@none@1@S@⌊#⌊>Lexical-functional grammar>⌋ (LFG)#⌋•⌋@@@@1@3@@oe@26-8-2013
1000008400440@unknown@formal@none@1@S@⌊=Categorial grammar¦3=⌋@@@@1@2@@oe@26-8-2013
1000008400450@unknown@formal@none@1@S@⌊>Categorial grammar>⌋ is an approach that attributes the syntactic structure not to rules of grammar, but to the properties of the ⌊>syntactic categories>⌋ themselves.@@@@1@24@@oe@26-8-2013
1000008400460@unknown@formal@none@1@S@For example, rather than asserting that sentences are constructed by a rule that combines a noun phrase (NP) and a verb phrase (VP) (e.g. the ⌊>phrase structure rule>⌋ S → NP VP), in categorial grammar, such principles are embedded in the category of the ⌊>head>⌋ word itself.@@@@1@47@@oe@26-8-2013
1000008400470@unknown@formal@none@1@S@So the syntactic category for an ⌊>intransitive>⌋ verb is a complex formula representing the fact that the verb acts as a ⌊>functor>⌋ which requires an NP as an input and produces a sentence level structure as an output.@@@@1@38@@oe@26-8-2013
1000008400480@unknown@formal@none@1@S@This complex category is notated as (NP\\S) instead of V.@@@@1@10@@oe@26-8-2013
1000008400490@unknown@formal@none@1@S@NP\\S is read as " a category that searches to the left (indicated by \\) for a NP (the element on the left) and outputs a sentence (the element on the right)".@@@@1@32@@oe@26-8-2013
1000008400500@unknown@formal@none@1@S@The category of ⌊>transitive verb>⌋ is defined as an element that requires two NPs (its subject and its direct object) to form a sentence.@@@@1@24@@oe@26-8-2013
1000008400510@unknown@formal@none@1@S@This is notated as (NP/(NP\\S)) which means "a category that searches to the right (indicated by /) for an NP (the object), and generates a function (equivalent to the VP) which is (NP\\S), which in turn represents a function that searches to the left for an NP and produces a sentence).@@@@1@51@@oe@26-8-2013
1000008400520@unknown@formal@none@1@S@⌊>Tree-adjoining grammar>⌋ is a categorial grammar that adds in partial ⌊>tree structure>⌋s to the categories.@@@@1@15@@oe@26-8-2013
1000008400530@unknown@formal@none@1@S@⌊=Dependency grammar¦3=⌋@@@@1@2@@oe@26-8-2013
1000008400540@unknown@formal@none@1@S@⌊>Dependency grammar>⌋ is a different type of approach in which structure is determined by the ⌊>relation>⌋s (such as ⌊>grammatical relation>⌋s) between a word (a ⌊/⌊>head>⌋/⌋) and its dependents, rather than being based in constituent structure.@@@@1@35@@oe@26-8-2013
1000008400550@unknown@formal@none@1@S@For example, syntactic structure is described in terms of whether a particular ⌊>noun>⌋ is the ⌊>subject>⌋ or ⌊>agent>⌋ of the ⌊>verb>⌋, rather than describing the relations in terms of trees (one version of which is the ⌊>parse tree>⌋) or other structural system.@@@@1@42@@oe@26-8-2013
1000008400560@unknown@formal@none@1@S@Some dependency-based theories of syntax:@@@@1@5@@oe@26-8-2013
1000008400570@unknown@formal@none@1@S@⌊•⌊#⌊>Algebraic syntax>⌋@@@@1@2@@oe@26-8-2013
1000008400580@unknown@formal@none@1@S@⌊>Word grammar>⌋@@@@1@2@@oe@26-8-2013
1000008400590@unknown@formal@none@1@S@⌊>Operator Grammar>⌋#⌋•⌋@@@@1@2@@oe@26-8-2013
1000008400600@unknown@formal@none@1@S@⌊=Stochastic/probabilistic grammars/network theories¦3=⌋@@@@1@3@@oe@26-8-2013
1000008400610@unknown@formal@none@1@S@Theoretical approaches to syntax that are based upon ⌊>probability theory>⌋ are known as ⌊>stochastic grammar>⌋s.@@@@1@15@@oe@26-8-2013
1000008400620@unknown@formal@none@1@S@One common implementation of such an approach makes use of a ⌊>neural network>⌋ or ⌊>connectionism>⌋.@@@@1@15@@oe@26-8-2013
1000008400630@unknown@formal@none@1@S@Some theories based within this approach are:@@@@1@7@@oe@26-8-2013
1000008400640@unknown@formal@none@1@S@⌊•⌊#⌊>Optimality theory>⌋@@@@1@2@@oe@26-8-2013
1000008400650@unknown@formal@none@1@S@⌊>Stochastic context-free grammar>⌋#⌋•⌋@@@@1@3@@oe@26-8-2013
1000008400660@unknown@formal@none@1@S@⌊=Functionalist grammars¦3=⌋@@@@1@2@@oe@26-8-2013
1000008400670@unknown@formal@none@1@S@Functionalist theories, although focused upon form, are driven by explanation based upon the function of a sentence (i.e. its communicative function).@@@@1@21@@oe@26-8-2013
1000008400680@unknown@formal@none@1@S@Some typical functionalist theories include:@@@@1@5@@oe@26-8-2013
1000008400690@unknown@formal@none@1@S@⌊•⌊#⌊>Functional grammar>⌋ (Dik)#⌋@@@@1@3@@oe@26-8-2013
1000008400700@unknown@formal@none@1@S@⌊#⌊>Prague Linguistic Circle>⌋@@@@1@3@@oe@26-8-2013
1000008400710@unknown@formal@none@1@S@⌊>Systemic functional grammar>⌋@@@@1@3@@oe@26-8-2013
1000008400720@unknown@formal@none@1@S@⌊>Cognitive grammar>⌋#⌋@@@@1@2@@oe@26-8-2013
1000008400730@unknown@formal@none@1@S@⌊#⌊>Construction grammar>⌋ (CxG)#⌋@@@@1@3@@oe@26-8-2013
1000008400740@unknown@formal@none@1@S@⌊#⌊>Role and reference grammar>⌋ (RRG)#⌋•⌋@@@@1@5@@oe@26-8-2013
1000008500010@unknown@formal@none@1@S@⌊δText analyticsδ⌋@@@@1@2@@oe@26-8-2013
1000008500020@unknown@formal@none@1@S@The term ⌊∗text analytics∗⌋ describes a set of linguistic, lexical, pattern recognition, extraction, tagging/structuring, visualization, and predictive techniques.@@@@1@18@@oe@26-8-2013
1000008500030@unknown@formal@none@1@S@The term also describes processes that apply these techniques, whether independently or in conjunction with query and analysis of fielded, numerical data, to solve business problems.@@@@1@26@@oe@26-8-2013
1000008500040@unknown@formal@none@1@S@These techniques and processes discover and present knowledge – facts, business rules, and relationships – that is otherwise locked in textual form, impenetrable to automated processing.@@@@1@26@@oe@26-8-2013
1000008500050@unknown@formal@none@1@S@A typical application is to scan a set of documents written in a ⌊>natural language>⌋ and either model the document set for predictive classification purposes or populate a database or search index with the information extracted.@@@@1@36@@oe@26-8-2013
1000008500060@unknown@formal@none@1@S@Current approaches to text analytics use ⌊>natural language processing>⌋ techniques that focus on specialized domains.@@@@1@15@@oe@26-8-2013
1000008500070@unknown@formal@none@1@S@Typical subtasks are:@@@@1@3@@oe@26-8-2013
1000008500080@unknown@formal@none@1@S@⌊•⌊#⌊>Named Entity Recognition>⌋: recognition of entity names (for people and organizations), place names, temporal expressions, and certain types of numerical expressions.#⌋@@@@1@21@@oe@26-8-2013
1000008500090@unknown@formal@none@1@S@⌊#⌊>Coreference>⌋: identification chains of ⌊>noun phrase>⌋s that refer to the same object.@@@@1@12@@oe@26-8-2013
1000008500100@unknown@formal@none@1@S@For example, ⌊>anaphora>⌋ is a type of coreference.#⌋@@@@1@8@@oe@26-8-2013
1000008500110@unknown@formal@none@1@S@⌊#⌊>Relationship Extraction>⌋: extraction of named relationships between entities in text#⌋•⌋@@@@1@10@@oe@26-8-2013