ridm@nrct.go.th   ระบบคลังข้อมูลงานวิจัยไทย   รายการโปรดที่คุณเลือกไว้

Recognition of visual speech elements using adaptively boosted hidden Markov models

หน่วยงาน Nanyang Technological University, Singapore

รายละเอียด

ชื่อเรื่อง : Recognition of visual speech elements using adaptively boosted hidden Markov models
นักวิจัย : Foo, Say Wei , Lian, Yong , Dong, Liang
คำค้น : -
หน่วยงาน : Nanyang Technological University, Singapore
ผู้ร่วมงาน : -
ปีพิมพ์ : 2547
อ้างอิง : Foo, 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. , 1051-8215 , http://hdl.handle.net/10220/4584 , http://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%20models , http://dx.doi.org/10.1109/TCSVT.2004.826773
ที่มา : -
ความเชี่ยวชาญ : -
ความสัมพันธ์ : IEEE transactions on circuits and systems for video technology
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

The 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.

บรรณานุกรม :
Foo, Say Wei , Lian, Yong , Dong, Liang . (2547). Recognition of visual speech elements using adaptively boosted hidden Markov models.
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Foo, Say Wei , Lian, Yong , Dong, Liang . 2547. "Recognition of visual speech elements using adaptively boosted hidden Markov models".
    กรุงเทพมหานคร : Nanyang Technological University, Singapore.
Foo, Say Wei , Lian, Yong , Dong, Liang . "Recognition of visual speech elements using adaptively boosted hidden Markov models."
    กรุงเทพมหานคร : Nanyang Technological University, Singapore, 2547. Print.
Foo, Say Wei , Lian, Yong , Dong, Liang . Recognition of visual speech elements using adaptively boosted hidden Markov models. กรุงเทพมหานคร : Nanyang Technological University, Singapore; 2547.