Data-Driven Impostor Selection for T-Norm Score Normalisation and the Background Dataset in SVM-Based Speaker Verification

Authors:
Mitchell Mclaren;Robbie Vogt;Brendan Baker;Sridha Sridharan
Affiliations:
Speech and Audio Research Laboratory, QUT, Brisbane, Australia;Speech and Audio Research Laboratory, QUT, Brisbane, Australia;Speech and Audio Research Laboratory, QUT, Brisbane, Australia;Speech and Audio Research Laboratory, QUT, Brisbane, Australia
Venue:
ICB '09 Proceedings of the Third International Conference on Advances in Biometrics
Year:
2009

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Abstract

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.