Mask estimation incorporating phase-sensitive information for speech enhancement

Abstract

For deep neural network (DNN)-based methods, the time-frequency (T-F) masks are commonly used as the training target. However, most of them do not focus on the phase information, while recent studies have revealed that incorporating phase information into the T-F mask can effectively help improve the speech quality of the enhanced speech. In this paper, we present two techniques to obtain the T-F mask considering phase information. In the first technique, the characteristics about spectral structures of two phase differences, which include the phase difference (PD) between clean and noisy speech and the PD between noise and noisy speech, are firstly discussed. Then, considering the specific characteristics of two PDs, a parametric ideal ratio mask (IRM) whose parameters are controlled by the cosines of the two aforementioned PDs is proposed, which is termed as a bounded IRM with phase constraint (BIRMP). In the second technique, an optimal estimator based on generalized maximum a posteriori (GMAP) probability of complex speech spectrum is proposed and defined as an optimal GMAP estimation of complexspectrum (OGMAPC). The OGMAPC estimator can dynamically adjust the scale of prior information of spectral magnitude and phase. Considering the difficult predictability of speech phase in the DNN-based method, the second technique exploits the spectral magnitude part of the OGMAPC estimator to calculate an optimal magnitude mask with the phase information and its ideal value is used for DNN training. The experiments show that the proposed methods can outperform the reference methods.