A boosted multi-HMM classifier for recognition of visual speech elements

A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of adaptive boosting technique. Experiments were carried out to identify the ba...

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Main Authors:	Foo, Say Wei, 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
Online Access:	https://hdl.handle.net/10356/90750 http://hdl.handle.net/10220/4590 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:PUBMED&id=doi:10.1080/02699200400026884&genre=&isbn=&issn=0269-9206&date=&volume=20&issue=2-3&spage=149&epage=56&aulast=Parker&aufirst=%20Mark&auinit=&title=Clin%20Linguist%20Phon&atitle=Automatic%20speech%20recognition%20and%20training%20for%20severely%20dysarthric%20users%20of%20assistive%20technology%3A%20the%20STARDUST%20project%2E
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-907502020-03-07T13:24:46Z A boosted multi-HMM classifier for recognition of visual speech elements Foo, Say Wei Dong, Liang School of Electrical and Electronic Engineering IEEE International Conference on Acoustics, Speech and Signal Processing (2003 : Hong Kong) DRNTU::Engineering::Electrical and electronic engineering A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of adaptive boosting technique. Experiments were carried out to identify the basic visual speech elements in English using the proposed boosted classifier. Comparing the results obtained using the proposed classifier and those obtained using the traditional single HMM classifier, it may be said that the proposed system is significantly better in terms of accuracy and robustness. Accepted version 2009-04-29T03:28:06Z 2019-12-06T17:53:18Z 2009-04-29T03:28:06Z 2019-12-06T17:53:18Z 2003 2003 Conference Paper Foo, S. W. & Dong, L. (2003). A boosted multi-HMM classifier for recognition of visual speech elements. IEEE International Conference on Acoustics, Speech and Signal Proceeding 2003 (pp. 285-288). Singapore: School of Electrical and Electronic Engineering. https://hdl.handle.net/10356/90750 http://hdl.handle.net/10220/4590 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:PUBMED&id=doi:10.1080/02699200400026884&genre=&isbn=&issn=0269-9206&date=&volume=20&issue=2-3&spage=149&epage=56&aulast=Parker&aufirst=%20Mark&auinit=&title=Clin%20Linguist%20Phon&atitle=Automatic%20speech%20recognition%20and%20training%20for%20severely%20dysarthric%20users%20of%20assistive%20technology%3A%20the%20STARDUST%20project%2E 10.1080/02699200400026884 en © 2003 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. http://www.ieee.org/portal/site 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. 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
spellingShingle	DRNTU::Engineering::Electrical and electronic engineering Foo, Say Wei Dong, Liang A boosted multi-HMM classifier for recognition of visual speech elements
description	A novel boosted classifier using multiple Hidden Markov Models (HMMs) is reported in this paper. The composite HMMs are specially trained to highlight certain group of training samples with the application of adaptive boosting technique. Experiments were carried out to identify the basic visual speech elements in English using the proposed boosted classifier. Comparing the results obtained using the proposed classifier and those obtained using the traditional single HMM classifier, it may be said that the proposed system is significantly better in terms of accuracy and robustness.
author2	School of Electrical and Electronic Engineering
author_facet	School of Electrical and Electronic Engineering Foo, Say Wei Dong, Liang
format	Conference or Workshop Item
author	Foo, Say Wei Dong, Liang
author_sort	Foo, Say Wei
title	A boosted multi-HMM classifier for recognition of visual speech elements
title_short	A boosted multi-HMM classifier for recognition of visual speech elements
title_full	A boosted multi-HMM classifier for recognition of visual speech elements
title_fullStr	A boosted multi-HMM classifier for recognition of visual speech elements
title_full_unstemmed	A boosted multi-HMM classifier for recognition of visual speech elements
title_sort	boosted multi-hmm classifier for recognition of visual speech elements
publishDate	2009
url	https://hdl.handle.net/10356/90750 http://hdl.handle.net/10220/4590 http://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:PUBMED&id=doi:10.1080/02699200400026884&genre=&isbn=&issn=0269-9206&date=&volume=20&issue=2-3&spage=149&epage=56&aulast=Parker&aufirst=%20Mark&auinit=&title=Clin%20Linguist%20Phon&atitle=Automatic%20speech%20recognition%20and%20training%20for%20severely%20dysarthric%20users%20of%20assistive%20technology%3A%20the%20STARDUST%20project%2E
_version_	1681045768277852160

A boosted multi-HMM classifier for recognition of visual speech elements

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