NDLI: On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

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On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Qiang Huo Chin-Hui Lee
Copyright Year	1993
Abstract	We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.
Starting Page	386
Ending Page	397
Page Count	12
File Size	365941
File Format	PDF
ISSN	10636676
Volume Number	6
Issue Number	4
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-07-01
Publisher Place	U.S.A.
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech recognition Automatic speech recognition Bayesian methods Loudspeakers Acoustic testing Approximation algorithms Acoustic transducers Recursive estimation Degradation
Content Type	Text
Resource Type	Article
Subject	Acoustics and Ultrasonics Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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