On the Competitive Neyman-Pearson Approach for Composite Hypothesis Testing and its Application in Voice Activity Detection

Abstract

The problem of composite hypothesis testing where the probability law governing the generation of the free parameter is not explicitly known is considered. It is shown that unlike the Neyman-Pearson (NP) approach, the competitive NP (CNP) approach models incomplete prior information about the source into the detector design by setting a variable upper bound for the probability of false-alarm term. Further, the CNP and NP approaches are employed to develop the CNP and NP detectors for voice activity detection (VAD), where the prior SNR is shown to be the free parameter of the composite hypothesis. We test the CNP and NP detectors using speech samples from the SWITCHBOARD database which are suitably corrupted using different noises and various SNRs. Our simulation results show that the CNP detector outperforms its NP counterpart and is comparable to the adaptive multi-rate (AMR) VADs.