Feature Selection Based on Information Theory for Speaker Verification

Authors:
Rafael Fernández;Jean-François Bonastre;Driss Matrouf;José R. Calvo
Affiliations:
Advanced Technologies Application Center, Havana, Cuba and Laboratoire d'Informatique d'Avignon, UAPV, France;Laboratoire d'Informatique d'Avignon, UAPV, France;Laboratoire d'Informatique d'Avignon, UAPV, France;Advanced Technologies Application Center, Havana, Cuba
Venue:
CIARP '09 Proceedings of the 14th Iberoamerican Conference on Pattern Recognition: Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
Year:
2009

Citing 4
Cited 0

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Abstract

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.