Górriz, J. M. and Ramírez, J. and Puntonet, Carlos G. and Lang, Elmar and Stadlthanner, K. (2006) Independent Component Analysis Applied to Voice Activity Detection. In: Alexandrov, Vassil N., (ed.) Computational science - ICCS 2006: 6th international conference, Reading, UK, May 28 - 31, 2006; Prodeedings, Part I. Lecture Notes in Computer Science, 3991. Springer, Berlin, pp. 234-241. ISBN 978-3-540-34380-6 ; 978-3-540-34379-0.
Full text not available from this repository.
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.
|Item Type:||Book Section|
|Institutions:||Biology, Preclinical Medicine > Institut für Biophysik und physikalische Biochemie > Prof. Dr. Elmar Lang|
|Subjects:||500 Science > 570 Life sciences|
|Created at the University of Regensburg:||Unknown|
|Deposited On:||13 Oct 2010 05:50|
|Last Modified:||13 Oct 2010 05:50|