Abstract
In this paper, we propose effective feature vectors to improve the performance of voice activity detection (VAD) employing a support vector machine (SVM), which is known to incorporate an optimized nonlinear decision over two different classes. To extract the effective feature vectors, we present a novel scheme combining the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD. Based on the results of experiments, the performance of the SVM-based VAD using novel feature vectors is found to be better than that of ITU-T G.729B and other recently reported methods.
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