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A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading

Abstract

Hidden Markov model (HMM) has been a popular mathematical approach for sequence classification such as speech recognition since 1980s. In this paper, a novel two-channel training strategy is proposed for discriminative training of HMM. For the proposed training strategy, a novel separable-distance function that measures the difference between a pair of training samples is adopted as the criterion function. The symbol emission matrix of an HMM is split into two channels: a static channel to maintain the validity of the HMM and a dynamic channel that is modified to maximize the separable distance. The parameters of the two-channel HMM are estimated by iterative application of expectation-maximization (EM) operations. As an example of the application of the novel approach, a hierarchical speaker-dependent visual speech recognition system is trained using the two-channel HMMs. Results of experiments on identifying a group of confusable visemes indicate that the proposed approach is able to increase the recognition accuracy by an average of 20% compared with the conventional HMMs that are trained with the Baum-Welch estimation.

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Correspondence to Liang Dong.

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Dong, L., Foo, S.W. & Lian, Y. A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading. EURASIP J. Adv. Signal Process. 2005, 347367 (2005). https://doi.org/10.1155/ASP.2005.1382

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Keywords and phrases

  • viseme recognition
  • two-channel hidden Markov model
  • discriminative training
  • separable-distance function
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