Paper Info
Title
Duration High-Order Hidden Markov Models and Training Algorithms for Speech Recognition.
Abstract
The duration high-order hidden Markov model (DHO-HMM) can capture the dynamic evolution of a physical system more precisely than can the first-order hidden Markov model (HMM). The relations among the DHO-HMM, high-order HMM (HO-HMM), hidden semi-Markov model (HSMM), and HMM are presented and discussed. Recursive forward and backward probability functions for the partial observation sequence were derived, and were used to calculate the expected number of state transitions and to update the DHO-HMM's parameters. Viterbi decoding and training algorithms for the DHO-HMM are also presented. Experimental results show that the proposed expectation-maximization (EM) training algorithm can obtain more reliable and accurate estimate of DHO-HMMs than the Viterbi training method. Experimental results also show that the DHO-HMM speech recognizer is superior to the HSMM and the baseline conventional HMM recognizers. In experiments, the DHO-HMM speech recognizer trained by the EM algorithm reduces recognition errors by up to 53% compared with the baseline HMM.
Year
Venue
Keywords
2015
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING
EM algorithm,hidden Markov model,high-order,speech recognition,duration modeling
Field
DocType
Volume
Physical system,Computer science,Artificial intelligence,Viterbi algorithm,Recursion,Pattern recognition,Expectation–maximization algorithm,Markov model,Algorithm,Speech recognition,Expected value,Viterbi decoder,Hidden Markov model
Journal
31
Issue
ISSN
Citations 
3
1016-2364
1
PageRank 
References 
Authors
0.39
11
1
Name
Order
Citations
PageRank
Lee-Min Lee1468.10