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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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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|Item Type:||Conference Paper|
|Keywords:||Speaker recognition, Data selection, Support vector machines, Gaussian Mixture Models|
|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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