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Speaker Identification Using Semi-supervised Learning

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Speech and Computer (SPECOM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9319))

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Abstract

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.

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Author information

Authors and Affiliations

  1. University of Patras, Patras, Greece

    Nikos Fazakis, Stamatis Karlos, Sotiris Kotsiantis & Kyriakos Sgarbas

Authors
  1. Nikos Fazakis
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  2. Stamatis Karlos
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  3. Sotiris Kotsiantis
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  4. Kyriakos Sgarbas
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Corresponding author

Correspondence to Nikos Fazakis .

Editor information

Editors and Affiliations

  1. SPIIRAS, Saint-Petersburg, Russia

    Andrey Ronzhin

  2. Moscow State Linguistic University, Moscow, Russia

    Rodmonga Potapova

  3. University of Patras, Patras, Greece

    Nikos Fakotakis

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© 2015 Springer International Publishing Switzerland

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Fazakis, N., Karlos, S., Kotsiantis, S., Sgarbas, K. (2015). Speaker Identification Using Semi-supervised Learning. In: Ronzhin, A., Potapova, R., Fakotakis, N. (eds) Speech and Computer. SPECOM 2015. Lecture Notes in Computer Science(), vol 9319. Springer, Cham. https://doi.org/10.1007/978-3-319-23132-7_48

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  • DOI: https://doi.org/10.1007/978-3-319-23132-7_48

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23131-0

  • Online ISBN: 978-3-319-23132-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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