Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/90658
Title: A two-channel training algorithm for hidden Markov model to identify visual speech elements
Authors: Foo, Say Wei
Yong, Lian
Dong, Liang
Keywords: DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Issue Date: 2003
Source: Foo, S. W., Yong, L., & Dong, L. (2003). A two-channel training algorithm for hidden Markov model to identify visual speech elements. In Proceedings of the International Symposium on Circuits and Systems 2003: (pp.572-575). Singapore.
Conference: IEEE International Symposium on Circuits and Systems (2003 : Bangkok, Thailand)
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.
URI: https://hdl.handle.net/10356/90658
http://hdl.handle.net/10220/5843
DOI: 10.1109/ISCAS.2003.1206038
Schools: School of Electrical and Electronic Engineering 
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Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:EEE Conference Papers

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