Design and performance analysis of Bayesian, Neyman-Pearson, and competitive Neyman-Pearson voice activity detectors

In this paper, the Bayesian, Neyman-Pearson (NP), and competitive Neyman-Pearson (CNP) detection approaches are analyzed using a perceptually modified Ephraim-Malah (EM) model, based on which a few practical voice activity detectors are developed. The voice activity detection is treated as a composite hypothesis testing problem with a free parameter formed by the prior signal-to-noise ratio (SNR). It is revealed that a high prior SNR is more likely to be associated with the "speech hypothesis" than the "pause hypothesis" and vice versa, and the CNP approach exploits this relation by setting a variable upper bound for the probability of false alarm. The proposed voice activity detectors (VADs) are tested under different noises and various SNRs, using speech samples from the Switchboard database and are compared with adaptive multirate (AMR) VADs. Our results show that the CNP VAD outperforms the NP and Bayesian VADs and compares well to the AMR VADs. The CNP VAD is also computationally inexpensive, making it a good candidate for applications in communication systems.