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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| Other Authors: | |
| Format: | Conference Paper |
| Language: | English |
| Published: |
2009
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| 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 |
| _version_ | 1826127674713571328 |
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| author | Foo, Say Wei Dong, Liang |
| author2 | School of Electrical and Electronic Engineering |
| author_facet | School of Electrical and Electronic Engineering Foo, Say Wei Dong, Liang |
| author_sort | Foo, Say Wei |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T07:12:45Z |
| format | Conference Paper |
| id | ntu-10356/90750 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T07:12:45Z |
| publishDate | 2009 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Foo, Say Wei Dong, Liang A boosted multi-HMM classifier for recognition of visual speech elements |
| title | 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_short | A boosted multi-HMM classifier for recognition of visual speech elements |
| title_sort | boosted multi hmm classifier for recognition of visual speech elements |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| 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 |
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