Paper Info
Title
Piecewise linear high-order hidden Markov models and applications to speech recognition
Abstract
The hidden Markov models have been widely used in speech recognition systems. However, the conditional independence of the state output will force the output of a hidden Markov model to be a piecewise constant random sequence, which is not a good approximation for many real processes. In this paper, a piecewise linear high-order hidden Markov model is proposed to better approximate the real process. An expectation-maximization based algorithm was presented for the parameter estimation of the proposed model. Experiments on speech recognition of Mandarin digits were conducted to examine the effectiveness of the proposed method. Experimental results show that the proposed method can reduce the recognition error rate significantly compared to a baseline hidden Markov model.
Year
DOI
Venue
2015
10.1109/ICMLC.2015.7340952
2015 International Conference on Machine Learning and Cybernetics (ICMLC)
Keywords
Field
DocType
Expectation-maximization,Parameter estimation,High-order hidden Markov model,Speech recognition,Piecewise linear approximation
Markov process,Maximum-entropy Markov model,Forward algorithm,Computer science,Artificial intelligence,Hidden semi-Markov model,Pattern recognition,Markov model,Markov chain,Speech recognition,Variable-order Markov model,Hidden Markov model,Machine learning
Conference
Volume
ISSN
Citations 
1
2160-133X
1
PageRank 
References 
Authors
0.39
6
1
Name
Order
Citations
PageRank
Lee-Min Lee1468.10