Abstract:

This paper describes a robust voice activity detector using an acoustic Doppler radar device. The sensor is used to detect the dynamic status of the speaker's mouth. At the frequencies of operation, background noises are largely attenuated, rendering the device robust to external acoustic noises in most operating conditions. Unlike the other non-acoustic sensors, the device need not be taped to the speaker, making it more acceptable in most situations. In this paper, various fetures computed from the sensor output are exploited for voice activity detection. The best set of features is selected based on robustness analysis. A support vector machine classifier is used to make the final speech/non-speech decision. Experimental results show that the proposed doppler-based voice activity detector improves speech/non-speech classification accuracy over that obtained using speech alone. The most significant improvements happen in low signal-to-noise (SNR) environments.