Speaker recognition using adaptively boosted classifier

Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239) Pub Date : 2001-08-19 DOI:10.1109/TENCON.2001.949632

S. Foo, Eng Guan Lim

引用次数: 11

Abstract

A novel approach for speaker recognition is proposed. The system makes use of adaptive boosting (AdaBoost) and multilayer perceptrons (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.

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基于自适应增强分类器的说话人识别

提出了一种新的说话人识别方法。该系统利用自适应增强(AdaBoost)和多层感知器(MLP)作为封闭集、文本依赖的说话人识别的分类器。系统的性能使用从YOHO演讲者验证语料库中抽取的20名演讲者(10名男性和10名女性)的子集进行评估。结果表明，通过对分类器进行自适应增强，可以提高识别精度。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Proceedings of IEEE Region 10 International Conference on Electrical and Electronic Technology. TENCON 2001 (Cat. No.01CH37239)

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