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A kernel trick for sequences applied to text-independent speaker verification systems

机译:适用于与文本无关的说话者验证系统的序列的内核技巧

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摘要

This paper presents a principled SVM based speaker verification system. We propose a new framework and a new sequence kernel that can make use of any Mercer kernel at the frame level. An extension of the sequence kernel based on the Max operator is also proposed. The new system is compared to state-of-the-art GMM and other SVM based systems found in the literature on the Banca and Polyvar databases. The new system outperforms, most of the time, the other systems, statistically significantly. Finally, the new proposed framework clarifies previous SVM based systems and suggests interesting future research directions. (C) 2007 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
机译:本文提出了一种基于原理的支持向量机的说话人验证系统。我们提出了一个新框架和一个新序列内核,它们可以在帧级别使用任何Mercer内核。还提出了基于Max运算符的序列内核扩展。将该新系统与Banca和Polyvar数据库文献中的最新GMM和其他基于SVM的系统进行了比较。从统计上看,新系统在大多数情况下都优于其他系统。最后,新提出的框架阐明了以前基于SVM的系统,并提出了有趣的未来研究方向。 (C)2007模式识别学会。由Elsevier Ltd.出版。保留所有权利。

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