DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yun, SJ | ko |
dc.contributor.author | Oh, Yung-Hwan | ko |
dc.date.accessioned | 2010-03-22T08:53:34Z | - |
dc.date.available | 2010-03-22T08:53:34Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 1999-02 | - |
dc.identifier.citation | IEEE SIGNAL PROCESSING LETTERS, v.6, no.2, pp.28 - 30 | - |
dc.identifier.issn | 1070-9908 | - |
dc.identifier.uri | http://hdl.handle.net/10203/17275 | - |
dc.description.abstract | To optimally cope with continuous speech recognizer, we propose the stochastic lexicon model that effectively represents variations in pronunciation, Zn this lexicon model, the baseform of a word is represented by subword-states with a probability distribution of subword units as a two-level hidden Markov model (HMM) and this baseform is automatically trained by sample utterances. Also, the proposed approach can be applied to systems employing nonlinguistic recognition units. | - |
dc.language | English | - |
dc.language.iso | en_US | en |
dc.publisher | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC | - |
dc.title | Stochastic lexicon modeling for speech recognition | - |
dc.type | Article | - |
dc.identifier.wosid | 000078061900002 | - |
dc.identifier.scopusid | 2-s2.0-0033079465 | - |
dc.type.rims | ART | - |
dc.citation.volume | 6 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 28 | - |
dc.citation.endingpage | 30 | - |
dc.citation.publicationname | IEEE SIGNAL PROCESSING LETTERS | - |
dc.embargo.liftdate | 9999-12-31 | - |
dc.embargo.terms | 9999-12-31 | - |
dc.contributor.localauthor | Oh, Yung-Hwan | - |
dc.contributor.nonIdAuthor | Yun, SJ | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | continuous speech recognition | - |
dc.subject.keywordAuthor | lexicon | - |
dc.subject.keywordAuthor | hidden Markov model | - |
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