DocumentCode
1032320
Title
Interframe dependent hidden Markov model for speech recognition
Author
Ming, Ji ; Smith, F.J.
Author_Institution
Dept. of Comput. Sci., Queen´s Univ., Belfast
Volume
30
Issue
3
fYear
1994
fDate
2/3/1994 12:00:00 AM
Firstpage
188
Lastpage
189
Abstract
A hidden Markov model (HMM) with first-order dependent observation densities is presented to account for the statistical dependence between successive frames. A modified Viterbi algorithm is described to optimise jointly the state sequence and dependence relation for the model parameter estimation as well as likelihood calculation. Preliminary experiments show that this approach achieves better performance than the standard multivariate Gaussian HMM
Keywords
hidden Markov models; parameter estimation; probability; speech recognition; HMM; dependence relation; first-order dependent observation densities; hidden Markov model; interframe dependent model; likelihood calculation; model parameter estimation; modified Viterbi algorithm; speech recognition; state sequence; statistical dependence;
fLanguage
English
Journal_Title
Electronics Letters
Publisher
iet
ISSN
0013-5194
Type
jour
DOI
10.1049/el:19940134
Filename
267254
Link To Document