NDLI: Speech recognition using hidden Markov model decomposition and a general background speech model

Content Provider	IEEE Xplore Digital Library
Author	Wang, M.Q. Young, S.J.
Copyright Year	1992
Description	Author affiliation: Dept. of Eng., Cambridge Univ., UK (Wang, M.Q.; Young, S.J.)
Abstract	Hidden Markov model (HMM) decomposition is used for recognizing speech in the presence of an interfering background speaker. The foreground speech is modeled by a set of left-to-right isolated word HMMs trained on a small isolated word database, and the background speech is modeled by a parallel ergodic HMM trained on a subset of TIMIT. The standard output approximation (OA) method of estimating the output probability distributions is used, and compared with a simple model combination (MC) technique. Recent work in this area has shown the effectiveness of vocabulary-specific background speech models, and hence this is used as a baseline. The results show that the general ergodic background model is as effective as a vocabulary-specific model. However, the MC technique is not effective.<>
Starting Page	253
Ending Page	256
File Size	379533
Page Count	4
File Format	PDF
ISBN	0780305329
ISSN	15206149
DOI	10.1109/ICASSP.1992.225924
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1992-03-23
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Speech recognition Viterbi algorithm Equations Probability distribution Vocabulary Filter bank High performance computing Arithmetic
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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