自动化学报2016，Vol.42Issue(6)：819-833,15.DOI:10.16383/j.aas.2016.c150734
基于深度学习语音分离技术的研究现状与进展
Deep Learning Based Speech Separation Technology and Its Developments
摘要
Abstract
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.关键词
神经网络/语音分离/计算听觉场景分析/机器学习Key words
Neural network/speech separation/computational auditory scene analysis/machine learning引用本文复制引用
刘文举,聂帅,梁山,张学良..基于深度学习语音分离技术的研究现状与进展[J].自动化学报,2016,42(6):819-833,15.基金项目
国家自然科学基金(61573357,61503382,61403370,61273267,91120303,61365006)资助Supported by National Natural Science Foundation of China (61573357,61503382,61403370,61273267,91120303,61365006) （61573357,61503382,61403370,61273267,91120303,61365006）
