Artificial Intelligence Review

, Volume 27, Issue 4, pp 295–307

An evaluation of one-class classification techniques for speaker verification

Authors

    • Department of Computer Science and InformaticsUniversity College Dublin
  • Marco Grimaldi
    • Department of Computer Science and InformaticsUniversity College Dublin
  • Pádraig Cunningham
    • Department of Computer Science and InformaticsUniversity College Dublin
Article

DOI: 10.1007/s10462-008-9071-8

Cite this article as:
Brew, A., Grimaldi, M. & Cunningham, P. Artif Intell Rev (2007) 27: 295. doi:10.1007/s10462-008-9071-8

Abstract

Speaker verification is a challenging problem in speaker recognition where the objective is to determine whether a segment of speech in fact comes from a specific individual. In supervised machine learning terms this is a challenging problem as, while examples belonging to the target class are easy to gather, the set of counter-examples is completely open. This makes it difficult to cast this as a supervised classification problem as it is difficult to construct a representative set of counter examples. So we cast this as a one-class classification problem and evaluate a variety of state-of-the-art one-class classification techniques on a benchmark speech recognition dataset. We construct this as a two-level classification process whereby, at the lower level, speech segments of 20 ms in length are classified and then a decision on an complete speech sample is made by aggregating these component classifications. We show that of the one-class classification techniques we evaluate, Gaussian Mixture Models shows the best performance on this task.

Keywords

One-class classifiersSpeaker verificationGaussian mixture models

Copyright information

© Springer Science+Business Media B.V. 2008