Recent Progresses in Deep Learning Based Acoustic Models

Dong Yu; Jinyu Li

doi:10.1109/JAS.2017.7510508

Volume 4 Issue 3

Jul. 2017

IEEE/CAA Journal of Automatica Sinica

JCR Impact Factor: 15.3, Top 1 (SCI Q1)

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Article Navigation > IEEE/CAA Journal of Automatica Sinica > 2017 > 4(3): 396-409

Dong Yu and Jinyu Li, "Recent Progresses in Deep Learning Based Acoustic Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 396-409, July 2017. doi: 10.1109/JAS.2017.7510508

Citation:

Dong Yu and Jinyu Li, "Recent Progresses in Deep Learning Based Acoustic Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 396-409, July 2017. doi: 10.1109/JAS.2017.7510508

Dong Yu and Jinyu Li, "Recent Progresses in Deep Learning Based Acoustic Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 396-409, July 2017. doi: 10.1109/JAS.2017.7510508

Citation:

Dong Yu and Jinyu Li, "Recent Progresses in Deep Learning Based Acoustic Models," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 396-409, July 2017. doi: 10.1109/JAS.2017.7510508

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Recent Progresses in Deep Learning Based Acoustic Models

doi: 10.1109/JAS.2017.7510508

Dong Yu^{1
,
,},
Jinyu Li^2
,

1.
with the Tencent AI Lab, Tencent, Bellevue, WA 98034, USA
2.
TMicrosoft AI and Research, Microsoft, Redmmond, WA 98052, USA

More Information

Author Bio:
Dong Yu is a distinguished scientist and vice general manager at Tencent AI Lab. Prior to joining Tencent, he was a principal researcher at Microsoft Research. His research has been focusing on speech recognition and other applications of machine learning techniques. He has published two monographs and more than 160 papers in these areas and is the coinventor of 50+ granted and 10+ pending patents and the leader of CNTK. His works have been recognized by the prestigious IEEE Signal Processing Society 2013 and 2016 best paper awards and the ACMSE 2005 best paper award. Dr. Dong Yu is currently serving as a member of the IEEE Speech and Language Processing Technical Committee (2013-2018), the vice chair of IEEE Seattle section (2017-), and the distinguished lecturer of APSIPA (2017-2018). He has served as an associate editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing (2011-2015), an associate editor of the IEEE Signal Processing Magazine (2008-2011), the lead guest editor of the IEEE Transactions on Audio, Speech, and Language Processing-special issue on deep learning for speech and language processing (2010-2011), a guest editor of the IEEE/CAA Journal of Automatica Sinica-special issue on deep learning in audio, image, and text processing (2015-2016), and members of organization and technical committees of many conferences and workshops.(e-mail: dongyu@ieee.org)

Jinyu Li received the bachelor and master degree from University of Science and Technology of China, in 1997 and 2000, with the highest honor, and the Ph.D. degree from Georgia Institute of Technology, Atlanta, in 2008. From 2000 to 2003, he was a researcher in the Intel China Research Center and Research Manager in iFlytek Speech, China. Currently, he is a principal applied scientist and technical lead in Microsoft Corporation, Redmond, WA. He leads a team to design and improve speech modeling algorithms and technologies that ensure industry state-of-the-art speech recognition accuracy for Microsoft products such as Cortana and xBox Kinect. His major research interests cover several topics in speech recognition, including deep learning, noise robustness, discriminative training, feature extraction, and machine learning methods. He authored more than 70 refereed publications and around 20 patents. He is the leading author of the book Robust Automatic Speech Recognition-A Bridge to Practical Applications, Academic Press, Oct, 2015. Currently, he serves as the associate editor of IEEE/ACM Transactions on Audio, Speech and Language Processing.(e-mail: jinyli@microsoft.com)
Corresponding author: Dong Yu, e-mail:dongyu@ieee.org
Received Date: 2017-04-11
Accepted Date: 2017-05-24

Abstract

Abstract

In this paper, we summarize recent progresses made in deep learning based acoustic models and the motivation and insights behind the surveyed techniques. We first discuss models such as recurrent neural networks (RNNs) and convolutional neural networks (CNNs) that can effectively exploit variablelength contextual information, and their various combination with other models. We then describe models that are optimized end-to-end and emphasize on feature representations learned jointly with the rest of the system, the connectionist temporal classification (CTC) criterion, and the attention-based sequenceto-sequence translation model. We further illustrate robustness issues in speech recognition systems, and discuss acoustic model adaptation, speech enhancement and separation, and robust training strategies. We also cover modeling techniques that lead to more efficient decoding and discuss possible future directions in acoustic model research.

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References(150)

References

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Recent Progresses in Deep Learning Based Acoustic Models

doi: 10.1109/JAS.2017.7510508

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