NDLI: Stochastic trajectory model with state-mixture for continuous speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Illina, I. Yifan Gong
Copyright Year	1996
Description	Author affiliation: Inst. Nat. de Recherche en Inf. et Autom., Vandoeuvre-les-Nancy, France (Illina, I.)
Abstract	The problem of acoustic modeling for continuous speech recognition is addressed. To deal with coarticulation effects and interspeaker variability, an extension of the mixture stochastic trajectory model (MSTM) is proposed. MSTM is a segment-based model using phonemes as speech units. In MSTM, the observations of a phoneme are modeled by a set of stochastic trajectories. The trajectories are modeled by a mixture of probability density functions (pdf) of state sequences. Each state is associated with a multivariate Gaussian density function. We propose to replace the state single Gaussian pdf by a mixture of Gaussian pdfs (MSTM with state-mixture, SM-MSTM). The parameters of the model are estimated under the ML criterion, using the expectation-maximisation (EM) algorithm. The tests of the system on a speaker-dependent continuous speech recognition task show a reduction in the word error rate by about 15% over the baseline MSTM, even for an equal number of parameters. Experiments based on a multispeaker continuous speech recognition task do not lead to significant improvement over the baseline system.
Starting Page	342
Ending Page	345
File Size	363298
Page Count	4
File Format	PDF
ISBN	0780335554
DOI	10.1109/ICSLP.1996.607124
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1996-10-03
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Loudspeakers Maximum likelihood estimation System testing Mesons Stochastic processes Hidden Markov models Speech recognition Density functional theory Polynomials Context modeling
Content Type	Text
Resource Type	Article

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