NDLI: A stochastic articulatory-to-acoustic mapping as a basis for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Hodgen, J. Valdez, P.
Copyright Year	2001
Description	Author affiliation: Modeling, Algorithms & Inf. Group, Los Alamos Nat. Lab., NM, USA (Hodgen, J.)
Abstract	Hidden Markov models of speech acoustics are the current state-of-the-art in speech recognition, but these models bear little resemblance to the processes underlying speech production. We describe two stochastic models of speech production: maximum likelihood continuity mapping (MALCOM) and conditional observable maximum likelihood continuity mapping (CO-MALCOM). The main component of these models is a stochastic mapping between acoustics and speech articulation. Perhaps surprisingly, the parameters of the stochastic mapping can be found using only acoustic data. We discuss theoretical and experimental reasons to believe that MALCOM and CO-MALCOM learn a stochastic mapping between articulator positions and speech acoustics. CO-MALCOM can be combined with standard recognition algorithms to do speech recognition based on a production model.
Sponsorship	IEEE Instrum. & Meas. Soc.
Starting Page	1105
Ending Page	1110
File Size	631208
Page Count	6
File Format	PDF
ISBN	0780366468
ISSN	10915281
DOI	10.1109/IMTC.2001.928251
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2001-05-21
Publisher Place	Hungary
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Stochastic processes Speech recognition Hidden Markov models Acoustics Speech processing Maximum likelihood estimation Automatic speech recognition Informatics Laboratories World Wide Web
Content Type	Text
Resource Type	Article
Subject	Instrumentation Electrical and Electronic Engineering

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