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Speech recognition using adaptively boosted classifier

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Speech recognition using adaptively boosted classifier

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Title: Speech recognition using adaptively boosted classifier
Author: Foo, Say Wei; Lim, Eng Guan
Copyright year: 2003
Abstract: In this paper, a novel approach for speaker recognition is proposed. The system makes use of adaptive boosting (AdaBoost)and multilayer perceptions (MLP) as classifier for closed set, text-dependent speaker recognition. The performance of the systems is assessed using a subset of 20 speakers, 10 male and 10 female, drawn from the YOHO speaker verification corpus. Results show that improvement in accuracy of recognition can be achieved through adaptive boosting of the classifier.
Type: Conference Paper
Conference name: International Conference on Electrical and Electronic Technology (2001)
Rights: © 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. 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: Published version

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