NDLI: Dynamic adaptation of hidden Markov model for robust speech recognition

Content Provider	IEEE Xplore Digital Library
Author	Gao Yu-Qing Chen Yong-Bin Wu Bo-Xiu
Copyright Year	1989
Description	Author affiliation: Inst. of Autom., Acad. of Sinica, Beijing, China (Gao Yu-Qing)
Abstract	An algorithm is presented for adaptation and self-learning of the hidden Markov model (HMM). It makes the HMM-based speech recognition robust, so that well-trained models can be adapted to new speaking conditions or a new speaker. The self-learning consists of the fact that, during recognition, all test tokens can be used to augment the current model. Both procedures increase the size of the training set. The algorithm was tested on a speaker-dependent speech recognition system for the whole Chinese vocabulary and a speaker-independent system for 0-9 digits. Experiments show that the algorithm is very successful, both for new-speaker adaptation and for variations of speech in a single speaker under various conditions.<>
Starting Page	1336
Ending Page	1339
File Size	279218
Page Count	4
File Format	PDF
DOI	10.1109/ISCAS.1989.100603
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1989-05-08
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Hidden Markov models Robustness Speech recognition Training data Vocabulary Pattern recognition Automation Adaptation model Automatic testing System testing
Content Type	Text
Resource Type	Article

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Dynamic adaptation of hidden Markov model for robust speech recognition