Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/69111
Title: A two-channel training algorithm for hidden Markov model to identify visual speech elements
Authors: Foo, S.W.
Lian, Y. 
Dong, L.
Issue Date: 2003
Source: Foo, S.W.,Lian, Y.,Dong, L. (2003). A two-channel training algorithm for hidden Markov model to identify visual speech elements. Proceedings - IEEE International Symposium on Circuits and Systems 2 : II572-II575. ScholarBank@NUS Repository.
Abstract: A novel two-channel algorithm is proposed in this paper for discriminative training of Hidden Markov Models (HMMs). It adjusts the symbol emission coefficients of an existing HMM to maximize the separable distance between a pair of confusable training samples. The method is applied to identify the visemes of visual speech, The results indicate that the two-channel training method provides better accuracy on separating similar visemes than the conventional Baum-Welch estimation.
Source Title: Proceedings - IEEE International Symposium on Circuits and Systems
URI: http://scholarbank.nus.edu.sg/handle/10635/69111
ISSN: 02714310
Appears in Collections:Staff Publications

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