A PROBABILISTIC APPROACH FOR PHASE ESTIMATION IN SINGLE-CHANNEL SPEECH ENHANCEMENT USING VON MISES PHASE PRIORS

In many artificial intelligence systems human voice is considered as the medium for information transmission. Human-machine communication by voice becomes difficult when speech is mixed with some background noise. As a remedy, a single-channel speech enhancement is indispensable for reducing background noise from noisy speech to make it suitable for automatic speech recognition and telephony speech. While the conventional techniques for single-channel speech enhancement incorporate noisy phase in both amplitude estimation and signal reconstruction stages, in this paper we propose a probabilistic method to estimate the clean speech phase from noisy observation. Our proposed method consists of phase unwrapping followed by threshold-based temporal smoothing using von Mises phase priors. The proposed phase enhancement method leads to improved speech quality and intelligibility predicted by instrumental measures without explicit incorporation of amplitude enhancement.