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

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

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Title: A boosted multi-HMM classifier for recognition of visual speech elements
Author: Foo, Say Wei; Dong, Liang
Copyright year: 2003
Abstract: 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.
Subject: DRNTU::Engineering::Electrical and electronic engineering
Type: Conference Paper
Conference name: IEEE International Conference on Acoustics, Speech and Signal Processing (2003 : Hong Kong)
School: School of Electrical and Electronic Engineering
Rights: © 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.
Version: Accepted version

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