Feature Selection Based on Information Theory for Speaker Verification

  • Rafael Fernández
  • Jean-François Bonastre
  • Driss Matrouf
  • José R. Calvo
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5856)


Feature extraction/selection is an important stage in every speaker recognition system. Dimension reduction plays a mayor roll due to not only the curse of dimensionality or computation time, but also because of the discriminative relevancy of each feature. The use of automatic methods able to reduce the dimension of the feature space without losing performance is one important problem nowadays. In this sense, a method based on mutual information is studied in order to keep as much discriminative information as possible and the less amount of redundant information. The system performance as a function of the number of retained features is studied.


mutual information feature selection speaker verification 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Rafael Fernández
    • 1
    • 2
  • Jean-François Bonastre
    • 2
  • Driss Matrouf
    • 2
  • José R. Calvo
    • 1
  1. 1.Advanced Technologies Application CenterHavanaCuba
  2. 2.Laboratoire d’Informatique d’AvignonUAPVFrance

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