Improved SVM speaker verification through data-driven background dataset collection

Authors:
Mitchell McLaren;Brendan Baker;Robbie Vogt;Sridha Sridharan
Affiliations:
Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia;Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia;Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia;Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia
Venue:
ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
Year:
2009

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Abstract

The problem of background dataset selection in SVM-based speaker verification is addressed through the proposal of a new data-driven selection technique. Based on support vector selection, the proposed approach introduces a method to individually assess the suitability of each candidate impostor example for use in the background dataset. The technique can then produce a refined background dataset by selecting only the most informative impostor examples. Improvements of 13% in min. DCF and 10% in EER were found on the SRE 2006 development corpus when using the proposed method over the best heuristically chosen set. The technique was also shown to generalise to the unseen NIST 2008 SRE corpus.