Neural Network Based Phase Compensation Methods on Monaural Speech Separation

Abstract

Monaural speech separation is a very challenging task, for the limitation of information from only a single channel. After Deep Learning is introduced into the monaural speech separation, researchers have made great improvement on the separation performance with the progress of different network structure. Among various of separation methods, especially methods with masking-based training targets, the influence of phase were always neglected during the separation process. In this paper, the impact of phase on monaural speech separation is discussed and proved using theoretical explanations and examples. New training targets in complement of existing magnitude training targets were trained through neural network methods to compensate for phase of target in order to achieve better separation performance. Comparisons and evaluations show that using phase compensation in separation boosts separation effect to a certain degree.