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Wavelet Speech Feature Extraction Using Mean Best Basis Algorithm

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Advances in Nonlinear Speech Processing (NOLISP 2009)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5933))

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Abstract

This paper presents Mean Best Basis algorithm, an extension of the well known Best Basis Wickerhouser’s method, for an adaptive wavelet decomposition of variable-length signals. A novel approach is used to obtain a decomposition tree of the wavelet-packet cosine hybrid transform for speech signal feature extraction. Obtained features are tested using the Polish language hidden Markov model phone classifier.

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Author information

Authors and Affiliations

  1. Department of Electronics, AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Jakub Gałka & Mariusz Ziółko

Authors
  1. Jakub Gałka
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  2. Mariusz Ziółko
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Editor information

Editors and Affiliations

  1. Department of Computer Science, Escola Politecnica Superior, Universidat de Vic, c/. Sagrada Familia, 7, 08500, Vic (Barcelona), Spain

    Jordi Solé-Casals

  2. Department of Computer Science, Escola Politecnica Superior, Universitat de Vic, c./. Sagrada Familia, 7, 08500, Vic (Barcelona), Spain

    Vladimir Zaiats

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Gałka, J., Ziółko, M. (2010). Wavelet Speech Feature Extraction Using Mean Best Basis Algorithm. In: Solé-Casals, J., Zaiats, V. (eds) Advances in Nonlinear Speech Processing. NOLISP 2009. Lecture Notes in Computer Science(), vol 5933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11509-7_17

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  • DOI: https://doi.org/10.1007/978-3-642-11509-7_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11508-0

  • Online ISBN: 978-3-642-11509-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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