SVM-Enabled voice activity detection

Authors:
Javier Ramírez;Pablo Yélamos;Juan Manuel Górriz;Carlos G. Puntonet;José C. Segura
Affiliations:
Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain;Dept. of Architecture and Computer Technology, University of Granada, Spain;Dept. of Signal Theory, Networking and Communications, University of Granada, Spain
Venue:
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
Year:
2006

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Abstract

Detecting the presence of speech in a noisy signal is an unsolved problem affecting numerous speech processing applications. This paper shows an effective method employing support vector machines (SVM) for voice activity detection (VAD) in noisy environments. The use of kernels in SVM enables to map the data into some other dot product space (called feature space) via a nonlinear transformation. The feature vector includes the subband signal-to-noise ratios of the input speech and a radial basis function (RBF) kernel is used as SVM model. It is shown the ability of the proposed method to learn how the signal is masked by the acoustic noise and to define an effective non-linear decision rule. The proposed approach shows clear improvements over standardized VADs for discontinuous speech transmission and distributed speech recognition, and other recently reported VADs.