INTERFRAME DEPENDENT HIDDEN MARKOV MODEL FOR SPEECH RECOGNITION

被引：2
|
作者：
MING, J
SMITH, FJ
机构：
[1] Computer Science Department, Queen's University
来源：
ELECTRONICS LETTERS | 1994年 / 30卷 / 03期
关键词：
SPEECH RECOGNITION; MODELING;
D O I：
10.1049/el:19940134
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
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 muitivariate Gaussian HMM.
引用
收藏
页码：188 / 189
页数：2
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