Independent Component Analysis Applied to Voice Activity Detection

  • J. M. Górriz
  • J. Ramírez
  • C. G. Puntonet
  • E. W. Lang
  • K. Stadlthanner
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3991)


In this paper we present the first application of Independent Component Analysis (ICA) to Voice Activity Detection (VAD). The accuracy of a multiple observation-likelihood ratio test (MO-LRT) VAD is improved by transforming the set of observations to a new set of independent components. Clear improvements in speech/non-speech discrimination accuracy for low false alarm rate demonstrate the effectiveness of the proposed VAD. It is shown that the use of this new set leads to a better separation of the speech and noise distributions, thus allowing a more effective discrimination and a tradeoff between complexity and performance. The algorithm is optimum in those scenarios where the loss of speech frames could be unacceptable, causing a system failure. The experimental analysis carried out on the AURORA 3 databases and tasks provides an extensive performance evaluation together with an exhaustive comparison to the standard VADs such as ITU G.729, GSM AMR and ETSI AFE for distributed speech recognition (DSR), and other recently reported VADs.


Independent Component Analysis Blind Source Separation Speech Recognition System Voice Activity Detection Speech Frame 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • J. M. Górriz
    • 1
  • J. Ramírez
    • 1
  • C. G. Puntonet
    • 2
  • E. W. Lang
    • 3
  • K. Stadlthanner
    • 3
  1. 1.Dpt. Signal Theory, Networking and communicationsUniversity of GranadaSpain
  2. 2.Dpt. Computer Architecture and TechnologyUniversity of GranadaSpain
  3. 3.AG Neuro- und BioinformatikUniversität RegensburgDeutschland

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