8th Annual Conference of the International Speech Communication Association

Antwerp, Belgium
August 27-31, 2007

Voice Activity Detection Based on Support Vector Machine Using Effective Feature Vectors

Q-Haing Jo, Yun-Sik Park, Kye-Hwan Lee, Ji-Hyun Song, Joon-Hyuk Chang

Inha University, Korea

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.

Full Paper

Bibliographic reference.  Jo, Q-Haing / Park, Yun-Sik / Lee, Kye-Hwan / Song, Ji-Hyun / Chang, Joon-Hyuk (2007): "Voice activity detection based on support vector machine using effective feature vectors", In INTERSPEECH-2007, 2937-2940.