A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

Q-Haing JO; Yun-Sik PARK; Kye-Hwan LEE; Joon-Hyuk CHANG

doi:10.1093/ietcom/e91-b.6.2090

Regular Section

A Support Vector Machine-Based Voice Activity Detection Employing Effective Feature Vectors

Q-Haing JO, Yun-Sik PARK, Kye-Hwan LEE, Joon-Hyuk CHANG

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Keywords: voice activity detection, support vector machine, a posteriori SNR, a priori SNR, predicted SNR

JOURNAL RESTRICTED ACCESS

2008 Volume E91.B Issue 6 Pages 2090-2093

DOI https://doi.org/10.1093/ietcom/e91-b.6.2090

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Abstract

In this letter, 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 that combines the a posteriori SNR, a priori SNR, and predicted SNR, widely adopted in conventional statistical model-based VAD.

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