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Title:
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A supervised two-channel learning method for hidden Markov model and application on lip reading.
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Author:
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Foo, Say Wei.; Dong, Liang.
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Copyright year:
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2002 |
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Abstract:
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In this paper, a novel two-channel learning method for hidden Markov model (HMM) is proposed. This method is specially designed to train HMMs for fine recognition
from similar observations. The prominent features of this method are 1.) the criterion function is based on the
difference between training sequences, and 2.) a twochannel structure is adopted to maintain the validity of the HMM. This learning method has been applied on a
viseme-level lip reading system. The result shows that the performance of the two channel approach is better than that of the maximum likelihood (ML) estimation. |
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Type:
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Conference Paper |
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Conference name:
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IEEE International Conference on Advanced Learning Technologies. |
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Rights:
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© 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. |
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Version:
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Accepted version |
