2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06) A Novel Statistical Model for Speech Recognition and POS Tagging Sydney, NSW, Australia November 22-November 24 ISBN: 0-7695-2688-8
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/AVSS.2006.9
Hidden Markov model is a statistical model which has been applied successfully to speech recognition and natural language processing. However, it is based on three assumptions: (1) limited horizon, (2) time invariant (stationary), (3)the independence assumption of observations within a state. These assumptions are too strong from the view of the statistics and are also unreaistic. In order to overcome the defects of the classical HMM, Markov Family model, a new statistical models is proposed in this paper. The speaker independent continuous speech recognition experiments and the Part-of-Speech tagging experiments show that Markov Family models (MFMs) have higher performance than Hidden Markov models (HMMs).
Citation:
Lichi Yuan, Zhigang Chen, "A Novel Statistical Model for Speech Recognition and POS Tagging," avss, pp.61, 2006 IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS'06), 2006 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
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