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A New Text Independent Speaker Recognition System with Short Utterances Using SVM

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Information Systems (EMCIS 2020)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 402))

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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 Support Vector Machines (SVM) can improve speaker recognition for short utterance data duration. The main contribution in this approach is the use of new vectors when training and testing data are limited. We show how different kernels function of SVM can be used to validate the new approach with different speakers from different databases.

R. Chakroun---No academic titles or descriptions of academic positions should be included in the addresses. The affiliations should consist of the author’s institution, town, and country.

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Author information

Authors and Affiliations

  1. Advanced Technologies for Image and Signal Processing (ATISP) Research Unit, Sfax, Tunisia

    Rania Chakroun & Mondher Frikha

  2. National School of Electronics and Telecommunications of Sfax, Sakiet Ezzit, Tunisia

    Mondher Frikha

  3. 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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Corresponding author

Correspondence to Rania Chakroun .

Editor information

Editors and Affiliations

  1. Department of Digital Innovation, School of Business, University of Nicosia, Nicosia, Cyprus

    Marinos Themistocleous

  2. British University in Dubai, Dubai, United Arab Emirates

    Maria Papadaki

  3. School of Strategy and Leadership, Coventry University, Coventry, UK

    Muhammad Mustafa Kamal

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Chakroun, R., Frikha, M. (2020). A New Text Independent Speaker Recognition System with Short Utterances Using SVM. In: Themistocleous, M., Papadaki, M., Kamal, M.M. (eds) Information Systems. EMCIS 2020. Lecture Notes in Business Information Processing, vol 402. Springer, Cham. https://doi.org/10.1007/978-3-030-63396-7_38

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  • DOI: https://doi.org/10.1007/978-3-030-63396-7_38

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-63395-0

  • Online ISBN: 978-3-030-63396-7

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