Robust voice activity detection based on concept of modulation transfer function in noisy reverberant environments

Most of the current voice activity detection (VAD) algorithms deal with clean (noiseless) speech or speech with additive noise conditions. They cannot work in noisy reverberant environments or work poorly if they do, because speech is smeared due to the effects of noise and reverberation. This paper proposes a robust VAD algorithm for precisely detecting speech and non-speech periods in noisy reverberant environments. The proposed VAD algorithm consists of three blocks. The first block is an estimation of the signal to noise ratio (SNR) which is used to mitigate the additive noise effect on the speech power envelope. The second block is a speech power envelope dereverberation based on the modulation transfer function concept. The last block is a threshold processing on the dereverberated speech power envelope for speech/non-speech decision. Experiments on VAD in both artificial and realistic noisy reverberant environments revealed that the proposed VAD algorithm significantly outperforms the conventional VAD algorithms.