Presentation 2004/12/13
Production models for speech recognition
Erik MCDERMOTT,
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Abstract(in English) Acoustic modeling in speech recognition uses very little knowledge of the speech production process. At many levels our models continue to model speech as a surface phenomenon. Typically, hidden Markov model (HMM) parameters operate primarily in the acoustic space or in a linear transformation thereof; state-to-state evolution is modeled only crudely, with no explicit relationship between states, such as would be afforded by the use of phonetic features commonly used by linguists to describe speech phenomena, or by the continuity and smoothness of the production parameters governing speech. This article attempts to provide an overview of proposals by several researchers for improving acoustic modeling in these regards. Such topics as the controversial Motor Theory of Speech Perception, work by Hogden explicitly using a continuity constraint in a pseudo-articulatory domain, the Kalman filter based Hidden Dynamic Model, and work by many groups showing the benefits of using articulatory features instead of phones as the underlying units of speech, will be covered.
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Keyword(in English) speech recognition / speech production / articulatory modeling / linear dynamical systems
Paper # NLC2004-41,SP2004-81
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Committee NLC
Conference Date 2004/12/13(1days)
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Language ENG
Title (in Japanese) (See Japanese page)
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Title (in English) Production models for speech recognition
Sub Title (in English)
Keyword(1) speech recognition
Keyword(2) speech production
Keyword(3) articulatory modeling
Keyword(4) linear dynamical systems
1st Author's Name Erik MCDERMOTT
1st Author's Affiliation Nippon Telegraph and Telephone Corporation, NTT Communication Science Laboratories()
Date 2004/12/13
Paper # NLC2004-41,SP2004-81
Volume (vol) vol.104
Number (no) 538
Page pp.pp.-
#Pages 6
Date of Issue