Spectral Phase Estimation Based on Deep Neural Networks for Single Channel Speech Enhancement

Abstract

Majority of speech processing algorithms operate only with the spectral magnitude, leaving spectral phase unstructured and unexplored. With recent advancement in deep neural networks (DNNs), the phase processing became more important as an innovative and emergent prospective of the DNN based speech enhancement. In this paper, a speech enhancement method based on DNN combined with spectral phase estimation is proposed to improve the quality and intelligibility of the noisy speech. During training, DNNs are trained to learn a mapping from the noisy speech utterances and predict the coefficient to construct an ideal ratio mask for the spectral magnitude. The temporal smoothing unwrapped spectral phase estimation is incorporated as a target and transformed into a structured spectral phase during signal reconstruction. In enhancement stage, the enhanced speech magnitude is reconstructed with estimated structured spectral phase. Experimental results demonstrate success of the proposed method for speech enhancement in terms of the speech quality and intelligibility.