Speaker recognition using adaptively boosted decision tree classifier

Speaker recognition using adaptively boosted decision tree classifier

In this paper, a novel approach for speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and C4.5 decision trees for closed set, text-dependent speaker recognition. A subset of 20 speakers, 10 male and 10 female, drawn from the YOHO speaker verification corpus is...

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Bibliographic Details
Main Authors: Foo, Say Wei, Lim, Eng Guan
Other Authors: IEEE International Conference on Acoustics, Speech and Signal Processing (2002 : Orlando, Florida, US)
Format: Conference or Workshop Item
Language:English
Published: 2009
Online Access:https://hdl.handle.net/10356/79870
http://hdl.handle.net/10220/4615
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Institution: Nanyang Technological University
Language: English
Description
Summary:In this paper, a novel approach for speaker recognition is proposed. The approach makes use of adaptive boosting (AdaBoost) and C4.5 decision trees for closed set, text-dependent speaker recognition. A subset of 20 speakers, 10 male and 10 female, drawn from the YOHO speaker verification corpus is used to assess the performance of the system. Results reveal that an accuracy of 99.5% of speaker identification may be achieved.

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