Support vector machines and Joint Factor Analysis for speaker verification

  • Authors:
  • Najim Dehak;Patrick Kenny;Reda Dehak;Ondrej Glembek;Pierre Dumouchel;Lukas Burget;Valiantsina Hubeika;Fabio Castaldo

  • Affiliations:
  • Centre de recherche informatique de Montréal (CRIM), Canada;Centre de recherche informatique de Montréal (CRIM), Canada;Laboratoire de Recherche et de Développement de l'EPITA (LRDE), Paris, France;Speech@FIT group, Faculty of Information Technology, Brno University of Technology, Czech Republic;Centre de recherche informatique de Montréal (CRIM), Canada;Speech@FIT group, Faculty of Information Technology, Brno University of Technology, Czech Republic;Speech@FIT group, Faculty of Information Technology, Brno University of Technology, Czech Republic;Politecnico di Torino, Turin, Italy

  • Venue:
  • ICASSP '09 Proceedings of the 2009 IEEE International Conference on Acoustics, Speech and Signal Processing
  • Year:
  • 2009

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Abstract

This article presents several techniques to combine between Support vector machines (SVM) and Joint Factor Analysis (JFA) model for speaker verification. In this combination, the SVMs are applied to different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that using SVM in JFA factors gave the best results especially when within class covariance normalization method is applied in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.