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Voice Activity Detection Using Generalized Gamma Distribution

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Advances in Artificial Intelligence (SETN 2006)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 3955))

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Abstract

In this work, we model speech samples with a two-sided generalized Gamma distribution and evaluate its efficiency for voice activity detection. Using a computationally inexpensive maximum likelihood approach, we employ the Bayesian Information Criterion for identifying the phoneme boundaries in noisy speech.

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Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, Box 451, Thessaloniki, GR-54124, Greece

    George Almpanidis & Constantine Kotropoulos

Authors
  1. George Almpanidis
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  2. Constantine Kotropoulos
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Editor information

Editors and Affiliations

  1. Computer Science Department of University of Crete, Greece

    Grigoris Antoniou

  2. Institute of Computer Science, Foundation for Research & Technology – Hellas (FORTH), Vassilika Vouton, P.O. Box 1385, 71110, Heraklion, Greece

    George Potamias

  3. Institute of Informatics and Telecommunications, NCSR "Demokritos", 15310 A., Paraskevi Attikis, Greece

    Costas Spyropoulos

  4. Institute of Computer Science, FO.R.T.H., Vassilika Vouton, P.O. Box 1385, GR 71110, Heraklion, Greece

    Dimitris Plexousakis

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© 2006 Springer-Verlag Berlin Heidelberg

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Almpanidis, G., Kotropoulos, C. (2006). Voice Activity Detection Using Generalized Gamma Distribution. In: Antoniou, G., Potamias, G., Spyropoulos, C., Plexousakis, D. (eds) Advances in Artificial Intelligence. SETN 2006. Lecture Notes in Computer Science(), vol 3955. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752912_3

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  • DOI: https://doi.org/10.1007/11752912_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-34117-8

  • Online ISBN: 978-3-540-34118-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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