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Keyword Spotting Using Support Vector Machines

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Text, Speech and Dialogue (TSD 2002)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2448))

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Abstract

Support Vector Machines is a new and promising technique in statistical learning theory. Recently, this technique produced very interesting results in pattern recognition [1],[2],[3].

In this paper, one of the first application of Support Vector Machines (SVM) technique for the problem of keyword spotting is presented. It classifies the correct and the incorrect keywords by using linear and Radial Basis Function kernels. This is a first work proposed to use SVM in keyword spotting, in order to improve recognition and rejection accuracy. The obtained results are very promising.

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Author information

Authors and Affiliations

  1. LORIA-CNRS/ INRIA Lorraine, BP239, F54506, Vandoeuvre, France

    Yassine Ben Ayed, Dominique Fohr & Jean Paul Haton

  2. ENST, CNRS-LTCI, 46 rue Barrault, F75634, Paris cedex 13, France

    Gérard Chollet

Authors
  1. Yassine Ben Ayed
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  2. Dominique Fohr
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  3. Jean Paul Haton
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  4. Gérard Chollet
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Editor information

Editors and Affiliations

  1. Faculty of Informatics Department of Programming Systems and Communication, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Petr Sojka

  2. Faculty of Informatics Department of Information Technologies, Masaryk University, Botanická 68a, 602 00, Brno, Czech Republic

    Ivan Kopeček  & Karel Pala  & 

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© 2002 Springer-Verlag Berlin Heidelberg

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Ayed, Y.B., Fohr, D., Haton, J.P., Chollet, G. (2002). Keyword Spotting Using Support Vector Machines. In: Sojka, P., Kopeček, I., Pala, K. (eds) Text, Speech and Dialogue. TSD 2002. Lecture Notes in Computer Science(), vol 2448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46154-X_39

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  • DOI: https://doi.org/10.1007/3-540-46154-X_39

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-44129-8

  • Online ISBN: 978-3-540-46154-8

  • eBook Packages: Springer Book Archive

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