Independent component analysis applied to voice activity detection

  • Authors:

  • J. M. Górriz;J. Ramírez;C. G. Puntonet;E. W. Lang;K. Stadlthanner

  • Affiliations:

  • Dpt. Signal Theory, Networking and communications, University of Granada, Spain;Dpt. Signal Theory, Networking and communications, University of Granada, Spain;Dpt. Computer Architecture and Technology, University of Granada, Spain;AG Neuro- und Bioinformatik, Universität Regensburg, Deutschland;AG Neuro- und Bioinformatik, Universität Regensburg, Deutschland

  • Venue:
  • ICCS'06 Proceedings of the 6th international conference on Computational Science - Volume Part I
  • Year:
  • 2006

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Abstract

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.