Improvements on self-adaptive voice activity detector for telephone data

Voice activity detection (VAD) has been studied for many decades and energy VAD is most commonly used. Energy VAD performs well under noise-free environments but deteriorates under noisy environment. Self-adaptive VAD performs much better than the traditional energy VAD in many aspects. However, one issue is that, the single one minimum energy threshold of the self-adaptive AVD could not perform well under the conditions with different channel varieties or background noises. In this paper, we make several improvements on the self-adaptive VAD to deal with that issue and enhance the detection performances. A k-means based average energy clustering approach is proposed to find better minimum energy thresholds for each speech recording. In the VAD decision phase, the new threshold is used for the likelihood ratio test. Furthermore, better results have been achieved by applying the median filtering as a post-processing step of self-adaptive VAD to smooth the short-time noise VAD errors. Experimental results on a subset of the NIST 2006 speaker recognition evaluation dataset show that our proposed method outperforms both the traditional energy-based and self-adaptive VAD approaches.