CASIA OpenIR  > 模式识别国家重点实验室  > 语音交互
刘文举; 聂帅; 梁山
Source Publication自动化学报
Other AbstractNowadays, speech interaction technology has been widely used in our daily life. However, due to the interferences, 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.

Keyword语音分离 计算听觉场景分析 深度学习
Document Type期刊论文
Corresponding Author刘文举
Recommended Citation
GB/T 7714
刘文举,聂帅,梁山. 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.
APA 刘文举,聂帅,&梁山.(2016).基于深度学习语音分离技术的研究现状与进展.自动化学报,42(6),819-833.
MLA 刘文举,et al."基于深度学习语音分离技术的研究现状与进展".自动化学报 42.6(2016):819-833.
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