基于深度学习的语音识别技术现状与展望  

Deep Learning for Speech Recognition:Review of State-of-the-Arts Technologies and Prospects

作　　者：戴礼荣[1] 张仕良 黄智颖[1] Dai Lirong Zhang Shiliang Huang Zhiying(National Engineering Laboratory of Speech and Language Information Processing, University of Science and Technology of China, Hefei, 230027, Chin)

机构地区：[1]中国科学技术大学语音与语言信息处理国家工程实验室,合肥230027

出　　处：《数据采集与处理》2017年第2期221-231,共11页Journal of Data Acquisition and Processing

基　　金：安徽省科技重大专项(15czz02007)资助项目;国家重点研发计划(2016YFB1001300)资助项目

摘　　要：首先对深度学习的发展历史以及概念进行简要的介绍。然后回顾最近几年基于深度学习的语音识别的研究进展。这一部分内容主要分成以下5点进行介绍:声学模型训练准则,基于深度学习的声学模型结构,基于深度学习的声学模型训练效率优化,基于深度学习的声学模型说话人自适应和基于深度学习的端到端语音识别。最后就基于深度学习的语音识别未来可能的研究方向进行展望。In this paper,deep learning is briefly introduced.Then,a review of the research progress of deep learning based speech recognition is presented from the following five points：Training criterions for deep learning based acoustic models,different model architectures for deep learning based speech recognition acoustic modeling,scalable and distributed optimization methods for deep learning based acoustic model training,speaker adaptation for deep learning based acoustic model,and deep leaning based end-toend speech recognition.At the end of this paper,the future possible research points of deep learning based speech recognition are also proposed.

关 键 词：深度学习 深度神经网络 语音识别 说话人自适应 

分 类 号：TN912.3[电子电信—通信与信息系统]