Deep encoder/decoder dual-path neural network for speech separation in noisy reverberation environments

Abstract

In recent years, the speaker-independent, single-channel speech separation problem has made significant progress with the development of deep neural networks (DNNs). However, separating the speech of each interested speaker from an environment that includes the speech of other speakers, background noise, and room reverberation remains challenging. In order to solve this problem, a speech separation method for a noisy reverberation environment is proposed. Firstly, the time-domain end-to-end network structure of a deep encoder/decoder dual-path neural network is introduced in this paper for speech separation. Secondly, to make the model not fall into local optimum during training, a loss function stretched optimal scale-invariant signal-to-noise ratio (SOSISNR) was proposed, inspired by the scale-invariant signal-to-noise ratio (SISNR). At the same time, in order to make the training more appropriate to the human auditory system, the joint loss function is extended based on short-time objective intelligibility (STOI). Thirdly, an alignment operation is proposed to reduce the influence of time delay caused by reverberation on separation performance. Combining the above methods, the subjective and objective evaluation metrics show that this study has better separation performance in complex sound field environments compared to the baseline methods.