Performance analysis of wavelet subband based voice activity detection in cocktail party environment

In this paper, we analyze the performance of wavelet-based voice activity detection (VAD) algorithms with respect to the detection of target speech. In addition, the state-of-the-art VAD standardized for the G. 729 B, the ETSI AFE ES 202 050 are evaluated extensively. Experimental results on a self-built cocktail party corpus including different target-interference speech activity conditions are provided. Results show that: (i) the wavelet-based VAD algorithms are superior to other VADmethods in terms of classification measures; (ii) the robustness of the wavelet feature still holds in a completely mismatched environment.