Selection and analysis of HMM's state-number in speech recognition

It is known that the process of pronunciation is a stochastic process. From the viewpoint of information theory, it is an information source, which generates stochastic vectors of speech. While in recognition, the hidden Markov model (HMM) is another generator of the stochastic sequence. But for dif...

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Bibliographic Details
Main Authors: Zhang Jie, Huang Zhitong, Wang Xiaolan
Format: Conference Proceeding
Language:English
Subjects:
Automation
Hidden Markov models
Information entropy
Information theory
Shape
Speech analysis
Speech recognition
Statistical analysis
Stochastic processes
Uncertainty
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Summary:It is known that the process of pronunciation is a stochastic process. From the viewpoint of information theory, it is an information source, which generates stochastic vectors of speech. While in recognition, the hidden Markov model (HMM) is another generator of the stochastic sequence. But for different states of HMM, the distortion of above two information sources, pronunciation itself and HMM, is different. This paper establishes a simplified model to study the principle of selection of the number of the state in HMM. Finally, three conclusions on HMM information entropy are drawn. It is found that when the states of HMM amount to 6, the information entropy of HMM is very close to that of pronunciation itself. Therefore, the result is obtained that the number of the state in HMM of about 6 is the best selection.
DOI:10.1109/ICOSP.1998.770293