Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification

McLaren, Mitchell L., Vogt, Robert J., Baker, Brendan J., & Sridharan, Sridha (2009) Data-driven impostor selection for T-norm score normalisation and the background dataset in SVM-based speaker verification. In Massimo, Tistarelli & Nixon, Mark S. (Eds.) Advances in Biometrics : Third International Conferences, ICB 2009, Alghero, Italy, June 2-5, 2009, Proceedings. Springer , Berlin Heidelberg, pp. 474-483.

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A data-driven background dataset refinement technique was recently proposed for SVM based speaker verification. This method selects a refined SVM background dataset from a set of candidate impostor examples after individually ranking examples by their relevance. This paper extends this technique to the refinement of the T-norm dataset for SVM-based speaker verification. The independent refinement of the background and T-norm datasets provides a means of investigating the sensitivity of SVM-based speaker verification performance to the selection of each of these datasets. Using refined datasets provided improvements of 13% in min. DCF and 9% in EER over the full set of impostor examples on the 2006 SRE corpus with the majority of these gains due to refinement of the T-norm dataset. Similar trends were observed for the unseen data of the NIST 2008 SRE.

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ID Code: 29485
Item Type: Book Chapter
Additional URLs:
Keywords: Speaker recognition, Data selection, Support vector machines, Score Normalisation
DOI: 10.1007/978-3-642-01793-3_49
ISBN: 3642017924
ISSN: 0302-9743
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 Springer
Copyright Statement: The original publication is available at SpringerLink
Deposited On: 05 Jan 2010 22:31
Last Modified: 11 Oct 2015 16:23

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