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Efficient Parameterization for Automatic Speaker Recognition Using Support Vector Machines

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Book cover Intelligent Systems Design and Applications (ISDA 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 557))

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Abstract

Recent advances in the field of speaker recognition have proved to highly outperform algorithms. However this performance degrades when limited data are presented. This paper presents examples on how SVM can improve speaker recognition. The main contribution in this approach is the use of new low-dimensional vectors when training data are limited. We show how different kernels function of Support Vector Machines (SVM) can be used to deal a new approach for speaker recognition system. We achieved remarkable results using TIMIT database.

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Author information

Authors and Affiliations

  1. Advanced Technologies for Medicine and Signals (ATMS) Research Unit, National School of Engineering of Sousse, Sousse, Tunisia

    Leila Beltaïfa Zouari

  2. Advanced Technologies for Medicine and Signals (ATMS) Research Unit, National School of Electronics and Telecommunications of Sfax, Sfax, Tunisia

    Mondher Frikha

  3. Advanced Technologies for Medicine and Signals (ATMS) Research Unit, National School of Engineering of Sfax, Sfax, Tunisia

    Rania Chakroun

Authors
  1. Rania Chakroun
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  2. Mondher Frikha
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  3. Leila Beltaïfa Zouari
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Corresponding author

Correspondence to Rania Chakroun .

Editor information

Editors and Affiliations

  1. Departamento de Engenharia Informática, Instituto Superior de Engenharia do Port, Porto, Portugal

    Ana Maria Madureira

  2. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs, Auburn, Washington, USA

    Ajith Abraham

  3. Polytechnic Institute of Porto, Felgueiras, Portugal

    Dorabela Gamboa

  4. Campus of Gualtar, University of Minho, Braga, Portugal

    Paulo Novais

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Chakroun, R., Frikha, M., Zouari, L.B. (2017). Efficient Parameterization for Automatic Speaker Recognition Using Support Vector Machines. In: Madureira, A., Abraham, A., Gamboa, D., Novais, P. (eds) Intelligent Systems Design and Applications. ISDA 2016. Advances in Intelligent Systems and Computing, vol 557. Springer, Cham. https://doi.org/10.1007/978-3-319-53480-0_65

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  • DOI: https://doi.org/10.1007/978-3-319-53480-0_65

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-53479-4

  • Online ISBN: 978-3-319-53480-0

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