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Feature extraction and normalization in SVM speaker verification using telephone speech

University of Cape Town
Publication Date
  • Electrical Engineering
  • Communication
  • Ecology
  • Geography


In this research the Support Vector Machine classifier is applied to a text independent speaker verification task using conversational telephone speech from the NIST 2000 Speaker Recognition Evaluation. The SVM is a discriminative classifier with good generalization characteristics. It has been shown to perform as well as, and sometimes outperform the more widely used Gaussian Mixture Model. The SVM, like other classifiers is vulnerable to environmental noise, distortions from transmission over communication channels such as the telephone channel, and intersession variability.

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