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: 21 June 2005


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 recognitiontwo-channel hidden Markov modeldiscriminative trainingseparable-distance function

Authors’ Affiliations

Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore, Singapore


© Dong et al. 2005