SVM Speaker Verification Using Session Variability Modelling and GMM Supervectors

  • M. McLaren
  • R. Vogt
  • S. Sridharan
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4642)

Abstract

This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied during the extraction of GMM supervectors prior to SVM speaker model training and classification. Experiments performed on the NIST 2005 corpus show major improvements over the baseline GMM supervector SVM system.

Keywords

Support Vector Machine Equal Error Rate Speaker Recognition Universal Background Model Speaker Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • M. McLaren
    • 1
  • R. Vogt
    • 1
  • S. Sridharan
    • 1
  1. 1.Speech and Audio Research Laboratory, Queensland University of Technology, BrisbaneAustralia

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