Speaker Identification Using Semi-supervised Learning

  • Nikos FazakisEmail author
  • Stamatis Karlos
  • Sotiris Kotsiantis
  • Kyriakos Sgarbas
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9319)


Semi-supervised classification methods use available unlabeled data, along with a small set of labeled examples, to increase the classification accuracy in comparison with training a supervised method using only the labeled data. In this work, a new semi-supervised method for speaker identification is presented. We present a comparison with other well-known semi-supervised and supervised classification methods on benchmark datasets and verify that the presented technique exhibits better accuracy in most cases.


Semi-supervised learning Speaker identification Classification using labeled Unlabeled data 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Nikos Fazakis
    • 1
    Email author
  • Stamatis Karlos
    • 1
  • Sotiris Kotsiantis
    • 1
  • Kyriakos Sgarbas
    • 1
  1. 1.University of PatrasPatrasGreece

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