VOICE ACTIVITY DETECTION DIRECTED BY NOISE CLASSIFICATION

VAD for speech/non-speech classification

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 select SVM model
for VAD algorithm. The proposed VAD is tested with
TIMIT data artificially distorted by different additive noise
types.