Title :
Parameter re-estimation in semicontinuous hidden Markov modelling of speech with feedback to vector quantisation codebook
Author :
Huang, X.D. ; Jack, M.A. ; Ariki, Yasuo
Author_Institution :
Edinburgh Univ.
fDate :
10/27/1988 12:00:00 AM
Abstract :
The parameters of the semicontinuous hidden Markov model (SCHMM) can be re-estimated by allowing the codebook to be updated, thus achieving an optimised codebook/model combination. With the optimised codebook, the SCHMM can offer improved recognition accuracy in comparison to both the continuous and the discrete hidden Markov model
Keywords :
Markov processes; feedback; speech recognition; feedback; parameters; re-estimated; recognition accuracy; semicontinuous hidden Markov modelling; speech; vector quantisation codebook;
Journal_Title :
Electronics Letters
