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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|Item Type:||Book Chapter|
|Keywords:||Speaker recognition, Data selection, Support vector machines, Score Normalisation|
|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 http://www.springerlink.com|
|Deposited On:||06 Jan 2010 08:31|
|Last Modified:||18 Jul 2014 10:07|
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