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

作　　者：刘文举[1] 聂帅[1] 梁山[1] 张学良[2] 

机构地区：[1]中国科学院自动化研究所模式识别国家重点实验室,北京100190 [2]内蒙古大学计算机系,呼和浩特010021

出　　处：《自动化学报》2016年第6期819-833,共15页Acta Automatica Sinica

基　　金：国家自然科学基金(61573357;61503382;61403370;61273267;91120303;61365006)资助~~

摘　　要：现阶段,语音交互技术日益在现实生活中得到广泛的应用,然而,由于干扰的存在,现实环境中的语音交互技术远没有达到令人满意的程度.针对加性噪音的语音分离技术是提高语音交互性能的有效途径,几十年来,全世界范围内的许多研究者为此投入了巨大的努力,提出了很多实用的方法.特别是近年来,由于深度学习研究的兴起,基于深度学习的语音分离技术日益得到了广泛关注和重视,显露出了相当光明的应用前景,逐渐成为语音分离中一个新的研究趋势.目前已有很多基于深度学习的语音分离方法被提出,但是,对于深度学习语音分离技术一直以来都缺乏一个系统的分析和总结,不同方法之间的联系和区分也很少被研究.针对这个问题,本文试图对语音分离的主要流程和整体框架进行细致的分析和总结,从特征、模型以及目标三个方面对现有的前沿研究进展进行全面而深入的综述,最后对语音分离技术进行展望.Nowadays, 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.

关 键 词：神经网络 语音分离 计算听觉场景分析 机器学习 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程] TP181[自动化与计算机技术—控制科学与工程]