Speaker verification using speaker- and test-dependent fast score normalization

  • Authors:
  • Daniel Ramos-Castro;Julian Fierrez-Aguilar;Joaquin Gonzalez-Rodriguez;Javier Ortega-Garcia

  • Affiliations:
  • ATVS (Speech and Signal Processing Group), Escuela Politecnica Superior, Avda. Francisco Tomas y Valiente 11, Campus de Cantoblanco, Universidad Autonoma de Madrid, E-28049 Madrid, Spain;ATVS (Speech and Signal Processing Group), Escuela Politecnica Superior, Avda. Francisco Tomas y Valiente 11, Campus de Cantoblanco, Universidad Autonoma de Madrid, E-28049 Madrid, Spain;ATVS (Speech and Signal Processing Group), Escuela Politecnica Superior, Avda. Francisco Tomas y Valiente 11, Campus de Cantoblanco, Universidad Autonoma de Madrid, E-28049 Madrid, Spain;ATVS (Speech and Signal Processing Group), Escuela Politecnica Superior, Avda. Francisco Tomas y Valiente 11, Campus de Cantoblanco, Universidad Autonoma de Madrid, E-28049 Madrid, Spain

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2007

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Abstract

A novel score normalization scheme for speaker verification is presented. The proposed technique is based on the widely used test-normalization method (Tnorm), which compensates test-dependent variability using a fixed cohort of impostors. The new procedure selects a speaker-dependent subset of impostor models from the fixed cohort using a distance-based criterion. Selection of the sub-cohort is made using a distance measure based on a fast approximation of the Kullback-Leibler (KL) divergence for Gaussian mixture models (GMM). The proposed technique has been called KL-Tnorm, and outperforms Tnorm in computational efficiency. Experimental results using NIST 2005 Speaker Recognition Evaluation protocol also show a stable performance improvement of our method on standard speaker recognition systems.