DocumentCode
764991
Title
On-line adaptation of the SCHMM parameters based on the segmental quasi-Bayes learning for speech recognition
Author
Huo, Qiang ; Chan, Chorkin ; Lee, Chin-Hui
Author_Institution
Dept. of Comput. Sci., Hong Kong Univ., Hong Kong
Volume
4
Issue
2
fYear
1996
fDate
3/1/1996 12:00:00 AM
Firstpage
141
Lastpage
144
Abstract
On-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary
Keywords
Bayes methods; adaptive systems; hidden Markov models; learning systems; speech recognition; English alphabet vocabulary; SCHMM; SCHMM parameters; mean vectors; mixture coefficients; online speaker adaptation; segmental quasiBayes learning; semicontinuous hidden Markov model; speech recognition; Acoustic testing; Acoustic transducers; Bayesian methods; Computer science; Hidden Markov models; Loudspeakers; Robustness; Speech recognition; System testing; Vocabulary;
fLanguage
English
Journal_Title
Speech and Audio Processing, IEEE Transactions on
Publisher
ieee
ISSN
1063-6676
Type
jour
DOI
10.1109/89.486065
Filename
486065
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