Voice Activity Detection Based on Generalized Normal-Laplace Distribution Incorporating Conditional MAP

In this paper, we propose a novel voice activity detection (VAD) algorithm based on the generalized normal-Laplace (GNL) distribution to provide enhanced performance in adverse noise environments. Specifically, the probability density function (PDF) of a noisy speech signal is represented by the GNL distribution; the variance of the speech and noise of the GNL distribution are estimated using higher-order moments. After in-depth analysis of estimated variances, a feature that is useful for discrimination between speech and noise at low SNRs is derived and compared to a threshold to detect speech activity. To consider the inter-frame correlation of speech activity, the result from the previous frame is employed in the decision rule of the proposed VAD algorithm. The performance of our proposed VAD algorithm is evaluated in terms of receiver operating characteristics (ROC) and detection accuracy. Results show that the proposed method yields better results than conventional VAD algorithms.