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.
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Keywords
- Independent Component Analysis
- Blind Source Separation
- Speech Recognition System
- Voice Activity Detection
- Speech Frame
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References
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© 2006 Springer-Verlag Berlin Heidelberg
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Górriz, J.M., Ramírez, J., Puntonet, C.G., Lang, E.W., Stadlthanner, K. (2006). Independent Component Analysis Applied to Voice Activity Detection. In: Alexandrov, V.N., van Albada, G.D., Sloot, P.M.A., Dongarra, J. (eds) Computational Science – ICCS 2006. ICCS 2006. Lecture Notes in Computer Science, vol 3991. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11758501_35
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DOI: https://doi.org/10.1007/11758501_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-34379-0
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