DNN-Based Speech Separation with Joint Improved Distortion Constraints
Meng Gao, Ying Gao, Feng Pei · 2021
Considering that in different signal-to-noise ratio (SNR) regions, the amplification distortion caused by the overestimation of the gain function will have varying degrees of impact on the speech intelligibility, this paper proposes a deep neural network (DNN) speech separation with joint improved distortion constraints to further improve speech intelligibility. The algorithm first uses the segmented SNR to correct the overestimated gain function, and then combines the constraints of amplification distortion to further constrain the output amplitude of the estimated speech to obtain the final separated speech. Finally, the comparative experiments show that the proposed algorithm can significantly improve the speech quality and intelligibility of the separated speech.