Speaker Verification on Unbalanced Data with Genetic Programming

  • Róisín Loughran
  • Alexandros Agapitos
  • Ahmed Kattan
  • Anthony Brabazon
  • Michael O’Neill
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9597)

Abstract

Automatic Speaker Verification (ASV) is a highly unbalanced binary classification problem, in which any given speaker must be verified against everyone else. We apply Genetic programming (GP) to this problem with the aim of both prediction and inference. We examine the generalisation of evolved programs using a variety of fitness functions and data sampling techniques found in the literature. A significant difference between train and test performance, which can indicate overfitting, is found in the evolutionary runs of all to-be-verified speakers. Nevertheless, in all speakers, the best test performance attained is always superior than just merely predicting the majority class. We examine which features are used in good-generalising individuals. The findings can inform future applications of GP or other machine learning techniques to ASV about the suitability of feature-extraction techniques.

Keywords

Speaker verification Unbalanced data Genetic programming Feature selection 

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Róisín Loughran
    • 1
  • Alexandros Agapitos
    • 1
  • Ahmed Kattan
    • 2
  • Anthony Brabazon
    • 1
  • Michael O’Neill
    • 1
  1. 1.Natural Computing Research and Applications GroupUniversity College DublinDublinIreland
  2. 2.Computer Science DepartmentUm Al-Qura UniversityMeccaSaudi Arabia

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