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Improved GMM-based speaker verification using SVM-driven impostor dataset selection

McLaren, Mitchell L., Vogt, Robert J., Baker, Brendan J., & Sridharan, Sridha (2009) Improved GMM-based speaker verification using SVM-driven impostor dataset selection. In Proceedings of Interspeech 2009, International Speech Communication Association (ISCA), Brighton Centre, Brighton, pp. 1267-1270.

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Abstract

The problem of impostor dataset selection for GMM-based speaker verification is addressed through the recently proposed data-driven background dataset refinement technique. The SVM-based refinement technique selects from a candidate impostor dataset those examples that are most frequently selected as support vectors when training a set of SVMs on a development corpus. This study demonstrates the versatility of dataset refinement in the task of selecting suitable impostor datasets for use in GMM-based speaker verification. The use of refined Z- and T-norm datasets provided performance gains of 15% in EER in the NIST 2006 SRE over the use of heuristically selected datasets. The refined datasets were shown to generalise well to the unseen data of the NIST 2008 SRE.

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ID Code: 29484
Item Type: Conference Paper
Additional URLs:
Keywords: Speaker recognition, Data selection, Support vector machines, Gaussian Mixture Models
ISSN: 1990-9772
Divisions: Past > QUT Faculties & Divisions > Faculty of Built Environment and Engineering
Past > Institutes > Information Security Institute
Past > Schools > School of Engineering Systems
Copyright Owner: Copyright 2009 International Speech Communication Association
Deposited On: 06 Jan 2010 08:46
Last Modified: 01 Mar 2012 00:03

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