Markov Family Models and Its Applications

L. Yuan (PRC)


Markov Family models, Hidden Markov models, Part-of Speech Tagging, Speech recognition


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 model is introduced in this paper. We have successfully applied Markov Family model to speech recognition and proposed a novel speech recognition model which integrates the frame and segment based acoustic modeling techniques. 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).

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