Fuzzy clustering and Bayesian information criterion based threshold estimation for robust voice activity detection

In previous voice activity detection (VAD) approaches that use threshold, consistent accuracy cannot be achieved since the mean-value based and the histogram based threshold estimation algorithms are not robust. They strongly depend on the percentage of voice and background noise in the estimate interval. In this paper, fuzzy clustering and Bayesian information criterion are proposed to estimate the thresholds for VAD. Compared to previous algorithms, the new algorithm is more robust and heuristic-rules-free. It is insensitive to the estimated interval, and can maintain fast tracking speed of environment change when combined with online update. Experiment shows it works very well with energy features in both stationary and non-stationary environments.