Improved voice activity detection via contextual information and noise suppression

In this paper, we develop a contextual voice activity detection (VAD) scheme which combines both contextual and frame specific information to improve detection. Unlike many VAD algorithms which assume that the cues to activity lie within the frame alone, our scheme seeks information for activity in the current as well as the neighboring frames. The new approach provides good robustness in low SNR when the speech frame is corrupted and an alternate reliable source of activity information is necessary. Further, we present a simple noise suppression scheme to enhance the VAD performance at low SNR. The noise suppressor provides spectrally reshaped signal to the VAD. Finally, we combine the contextual VAD and the noise suppression scheme with a basic detector to form a comprehensive VAD. The proposed comprehensive VAD system is tested on speech samples from the SWITCHBOARD database. Various noises under different SNRs are added to the speech signals. Experimental results show that the proposed VAD outperforms the standard algorithm ETSI AMR VAD-1.