Likelihood ratio sign test for voice activity detection

Abstract

Voice activity detection (VAD) plays an important role on the performance of speech processing systems in adverse environments. Recently, statistical model-based VADs have demonstrated impressive performance. The study presents a novel decision test (named likelihood ratio sign test, LRST) for VAD by using sign test and Neyman–Pearson criterion to improve the performance of statistical model-based VAD. The proposed LRST is derived based on the likelihood ratios (LRs) calculated from multiple independent observations by incorporating the long-term speech information into the decision rule. An implementation of the LRST VAD is introduced by defining the LRST over a sliding window and calculating the LRs based on complex Gaussian distribution for an input signal. For experiments, the multiple-observation LRT (MO-LRT) VAD based on multiple observations is used as a reference owing to its outstanding performance compared with conventional VADs. The experimental results show that the proposed approach outperforms the MO-LRT VAD in various noise environments.