Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành hóa học dành cho các bạn yêu hóa học tham khảo đề tài: A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading | EURASIP Journal on Applied Signal Processing 2005 9 1382-1399 2005 Hindawi Publishing Corporation A Two-Channel Training Algorithm for Hidden Markov Model and Its Application to Lip Reading Liang Dong Department of Electrical and Computer Engineering National University of Singapore Singapore 119260 Email engp0564@ Say Wei Foo School of Electrical and Electronic Engineering Nanyang Technological University 50 Nanyang Avenue Singapore 639798 Email eswfoo@ Yong Lian Department of Electrical and Computer Engineering National University of Singapore Singapore 119260 Email eleliany@ Received 1 November 2003 Revised 12 May 2004 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. 1. INTRODUCTION The focus of most automatic speech recognition techniques is on .
