A two-channel training algorithm for hidden Markov model to identify visual speech elements

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 v...

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Main Authors:	Foo, Say Wei, Yong, Lian, Dong, Liang
Other Authors:	School of Electrical and Electronic Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2009
Subjects:	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
Online Access:	https://hdl.handle.net/10356/90658 http://hdl.handle.net/10220/5843
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Institution:	Nanyang Technological University
Language:	English

id	sg-ntu-dr.10356-90658
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spelling	sg-ntu-dr.10356-906582020-03-07T13:24:46Z A two-channel training algorithm for hidden Markov model to identify visual speech elements Foo, Say Wei Yong, Lian Dong, Liang School of Electrical and Electronic Engineering IEEE International Symposium on Circuits and Systems (2003 : Bangkok, Thailand) DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing 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. Published version 2009-07-29T08:36:07Z 2019-12-06T17:51:42Z 2009-07-29T08:36:07Z 2019-12-06T17:51:42Z 2003 2003 Conference Paper 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. https://hdl.handle.net/10356/90658 http://hdl.handle.net/10220/5843 10.1109/ISCAS.2003.1206038 en © IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site. 4 p. application/pdf
institution	Nanyang Technological University
building	NTU Library
country	Singapore
collection	DR-NTU
language	English
topic	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering::Electronic systems::Signal processing Foo, Say Wei Yong, Lian Dong, Liang A two-channel training algorithm for hidden Markov model to identify visual speech elements
description	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.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Foo, Say Wei Yong, Lian Dong, Liang
format	Conference or Workshop Item
author	Foo, Say Wei Yong, Lian Dong, Liang
author_sort	Foo, Say Wei
title	A two-channel training algorithm for hidden Markov model to identify visual speech elements
title_short	A two-channel training algorithm for hidden Markov model to identify visual speech elements
title_full	A two-channel training algorithm for hidden Markov model to identify visual speech elements
title_fullStr	A two-channel training algorithm for hidden Markov model to identify visual speech elements
title_full_unstemmed	A two-channel training algorithm for hidden Markov model to identify visual speech elements
title_sort	two-channel training algorithm for hidden markov model to identify visual speech elements
publishDate	2009
url	https://hdl.handle.net/10356/90658 http://hdl.handle.net/10220/5843
_version_	1681046696791900160

A two-channel training algorithm for hidden Markov model to identify visual speech elements

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