DocumentCode :
1118997
Title :
Speaker-independent isolated word recognition using multiple hidden Markov models
Author :
Zhang, Y. ; deSilva, C.J.S. ; Togneri, R. ; Alder, M. ; Attikiouzel, Y.
Author_Institution :
Centre for Intelligent Inf. Process. Syst., Western Australia Univ., Nedlands, WA, Australia
Volume :
141
Issue :
3
fYear :
1994
fDate :
6/1/1994 12:00:00 AM
Firstpage :
197
Lastpage :
202
Abstract :
A multi-HMM speaker-independent isolated word recognition system is described. In this system, three vector quantisation methods, the LBG algorithm, the EM algorithm, and a new MGC algorithm, are used for the classification of the speech space. These quantisations of the speech space are then used to produce three HMMs for each word in the vocabulary. In the recognition step, the Viterbi algorithm is used in the three subrecognisers. The log probabilities of the observation sequences matching-the models are multiplied by the weights determined by the recognition accuracies of individual subrecognisers and summed to give the log probability that the utterance is of a particular word in the vocabulary. This multi-HMM system results in a reduction of about 50% in the error rate in comparison with the single model system
Keywords :
hidden Markov models; probability; speech coding; speech recognition; vector quantisation; EM algorithm; LBG algorithm; MGC algorithm; Viterbi algorithm; error rate; hidden Markov models; log probabilities; observation sequences; recognition accuracies; speaker-independent isolated word recognition; speech space classification; vector quantisation; vocabulary;
fLanguage :
English
Journal_Title :
Vision, Image and Signal Processing, IEE Proceedings -
Publisher :
iet
ISSN :
1350-245X
Type :
jour
DOI :
10.1049/ip-vis:19941142
Filename :
296563
Link To Document :
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