Clustering Based Under-Sampling for Improving Speaker Verification Decisions Using AdaBoost

  • Hakan Altınçay
  • Cem Ergün
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3138)

Abstract

The class imbalance problem naturally occurs in some classification problems where the amount of training samples available for one class may be much less than that of another. In order to deal with this problem, random sampling based methods are generally used. This paper proposes a clustering based sampling technique to select a subset from the majority class involving much larger amount of training data. The proposed approach is verified in designing a post-classifier using AdaBoost to improve the speaker verification decisions. Experiments conducted on NIST99 speaker verification corpus have shown that in general, the proposed sampling technique provides better equal error rates (EER) than random sampling.

Keywords

Training Sample Gaussian Mixture Model Majority Class Minority Class Equal Error Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Hakan Altınçay
    • 1
  • Cem Ergün
    • 1
  1. 1.Advanced Technology Research and Development InstituteEastern Mediterranean UniversityGazi Mağusa KKTC, Mersin 10Turkey

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