Applying support vector machines to voice activity detection

A new voice activity detector (VAD) algorithm using support vector machines (SVM) is proposed in the paper, and the new VAD effectiveness is validated. The sequential minimal optimization (SMO) algorithm for fast training support vector machines is adopted. The proposed VAD algorithm via SVM (SVM-VAD) also uses the characteristic parameters set used by G.729 Annex B (G.729B) VAD. Comparing SVM-VAD with G729B VAD shows that it is effective for applying SVM to VAD. The new proposed VAD algorithm is integrated with G.729B instead of G.729B VAD, informal listening tests show that the integrated speech coding system has a little better efficiency over the G.729B VAD in perceptivity.