SVM speaker verification using session variability modelling and GMM supervectors

被引：0

作者：

McLaren, M. ^{[1
]}

Vogt, R. ^{[1
]}

Sridharan, S. ^{[1
]}

机构：

[1] Queensland Univ Technol, Speech & Aud Res Lab, Brisbane, Qld, Australia

来源：

ADVANCES IN BIOMETRICS, PROCEEDINGS | 2007年 / 4642卷

基金：

澳大利亚研究理事会;

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied during the extraction of GMM supervectors prior to SVM speaker model training and classification. Experiments performed on the NIST 2005 corpus show major improvements over the baseline GMM supervector SVM system.

引用

页码：1077 / +

页数：2

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