Optimally weighted maximum a posteriori probabilities based on minimum classification error for dual-microphone voice activity detection

Abstract

The dual-microphone voice activity detection (VAD) technique is proposed by applying discriminative weight training to achieve optimal weighting of spatial features available within the dual-microphone VAD. Since the motivation behind our method is to use the relevant spatial information available from the two microphones, we employ the phase difference, coherence, and power level difference ratio (PLDR) as a feature vector, and then use this feature vector to derive the maximum a posteriori (MAP) probabilities. Then, we combine each MAP probability based on a discriminative weight training, i.e., the minimum classification error (MCE) method to offer an optimal VAD decision in a spectral domain, which successfully represents the dynamic evolution of speech over time even in the non-stationary noise environments. The proposed dual-microphone VAD algorithm outperforms conventional dual microphone VAD methods based on only single feature among the PLDR, phase difference, and spectral coherence.