Deep learning based speech separation technology and its developments

Nowadays, speech interaction technology has been widely used in our daily life. However, due to the interfer- ences, the performances of speech interaction systems in real-world environments are far from being satisfactory. Speech separation technology has been proven to be an effective way to improve the performance of speech interaction in noisy environments. To this end, decades of efforts have been devoted to speech separation. There have been many methods proposed and a lot of success achieved. Especially with the rise of deep learning, deep learning-based speech separation has been proposed and extensively studied, which has been shown considerable promise and become a main research line. So far, there have been many deep learning-based speech separation methods proposed. However, there is little systematic analysis and summary on the deep learning-based speech separation technology. We try to give a detail analysis and summary on the general procedures and components of speech separation in this regard. Moreover, we survey a wide range of supervised speech separation techniques from three aspects: 1) features, 2) targets, 3) models. And finally we give some views on its developments.