In this method voice activity detection (VAD) is formulated as a two class classification problem using support vector machines (SVM). The proposed method combines a noise robust feature extraction process together with SVM models trained in different background noises for speech/nonspeech classification. A multi-class SVM is also used to classify background noises in order to SVM model for VAD algorithm. The proposed VAD is tested with TIMIT data artificially distorted by different additive noise types.

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Robust voice activity detection directed by noise classification.pdf
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