Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/91495
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dc.contributor.authorFoo, Say Weien
dc.contributor.authorLian, Yongen
dc.contributor.authorDong, Liangen
dc.date.accessioned2009-04-27T01:59:29Zen
dc.date.accessioned2019-12-06T18:06:40Z-
dc.date.available2009-04-27T01:59:29Zen
dc.date.available2019-12-06T18:06:40Z-
dc.date.copyright2004en
dc.date.issued2004en
dc.identifier.citationFoo, S. W., Lian, Y., & Dong, L. (2004). Recognition of visual speech elements using adaptively boosted hidden Markov models. IEEE Transactions on Circuits and Systems for Video Technology, 14(5), 693-705.en
dc.identifier.issn1051-8215en
dc.identifier.urihttps://hdl.handle.net/10356/91495-
dc.identifier.urihttp://hdl.handle.net/10220/4584en
dc.description.abstractThe performance of automatic speech recognition (ASR) system can be significantly enhanced with additional information from visual speech elements such as the movement of lips, tongue, and teeth, especially under noisy environment. In this paper, a novel approach for recognition of visual speech elements is presented. The approach makes use of adaptive boosting (AdaBoost) and hidden Markov models (HMMs) to build an AdaBoost-HMM classifier. The composite HMMs of the AdaBoost-HMM classifier are trained to cover different groups of training samples using the AdaBoost technique and the biased Baum–Welch training method. By combining the decisions of the component classifiers of the composite HMMs according to a novel probability synthesis rule, a more complex decision boundary is formulated than using the single HMM classifier. The method is applied to the recognition of the basic visual speech elements. Experimental results show that the AdaBoost-HMM classifier outperforms the traditional HMM classifier in accuracy, especially for visemes extracted from contexts.en
dc.format.extent13 p.en
dc.language.isoenen
dc.relation.ispartofseriesIEEE transactions on circuits and systems for video technologyen
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dc.titleRecognition of visual speech elements using adaptively boosted hidden Markov modelsen
dc.typeJournal Articleen
dc.identifier.openurlhttp://sfxna09.hosted.exlibrisgroup.com:3410/ntu/sfxlcl3?sid=metalib:EVII&id=doi:10.1109/TCSVT.2004.826773&genre=&isbn=&issn=10518215&date=2004&volume=14&issue=5&spage=693&epage=705&aulast=Foo&aufirst=%20Say%20Wei&auinit=&title=IEEE%20Transactions%20on%20Circuits%20and%20Systems%20for%20Video%20Technology&atitle=Recognition%20of%20visual%20speech%20elements%20using%20adaptively%20boosted%20hidden%20markov%20modelsen
dc.identifier.doi10.1109/TCSVT.2004.826773en
dc.description.versionPublished versionen
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