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  • Research Article
  • Open Access

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

EURASIP Journal on Advances in Signal Processing20052005:347367

  • Received: 1 November 2003
  • Published:


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.

Keywords and phrases

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

Authors’ Affiliations

Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 119260, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, 50 Nanyang Avenue, Singapore, 639798, Singapore


© Dong et al. 2005