Portrait of Li Deng

Li Deng

Partner Research Manager

About

Li Deng (IEEE M’89;SM’92;F’04) received the Bachelor degree from Univ. Science & Technology of China (USTC; Guo Mo-Ruo Awardee), and Master and Ph.D. degrees from the University of Wisconsin-Madison, US. He was an assistant professor (1989-1992), tenured associate professor (1992-1996) and Full Professor (1996-1999) at the University of Waterloo, Ontario, Canada. In 1999, he joined Microsoft Research, Redmond, USA, where currently he leads R&D of application-focused deep learning as a Partner Research Manager of its Deep Learning Technology Center. Since January 2016, he has also taken new responsibilities in the company as the Chief Scientist of AI in Microsoft’s Applications and Service Group (ASG). Since 2000, he has been Affiliate Full Professor and graduate committee member at the University of Washington, Seattle.

 

Projects

From Captions to Visual Concepts and Back

Established: April 9, 2015

We introduce a novel approach for automatically generating image descriptions. Visual detectors, language models, and deep multimodal similarity models are learned directly from a dataset of image captions. Our system is state-of-the-art on the official Microsoft COCO benchmark, producing a…

Acoustic Modeling

Established: January 29, 2004

Acoustic modeling of speech typically refers to the process of establishing statistical representations for the feature vector sequences computed from the speech waveform. Hidden Markov Model (HMM) is one most common type of acoustuc models. Other acosutic models include segmental models, super-segmental models…

Whistler Text-to-Speech Engine

Established: November 5, 2001

The talking computer HAL in the 1968 film "2001-A Space Odyssey" had an almost human voice, but it was the voice of an actor, not a computer. Getting a real computer to talk like HAL has proven one of the…

Publications

2016

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Projects

Deep Learning for Text Processing Link description

Deep Learning for Text Processing

Date

August 4, 2014

Speakers

Li Deng, Eric Xing, Xiaodong He, Jianfeng Gao, Christopher Manning, Paul Smolensky, and Jeff A Bilmes

Affiliation

MSR, Carnegie Mellon University, Microsoft Research, Redmond, MSR Redmond, Stanford, Johns Hopkins University, University of Washington

Other

Biography

Prior to joining Microsoft, he also conducted research and taught at Massachusetts Institute of Technology, ATR Interpreting Telecommunications Research Lab. (Kyoto, Japan), and HKUST. He has been granted over 70 US or international patents in acoustics/audio, speech/language technology, large-scale natural language and enterprise/internet data analysis, and in machine learning with recent focus on deep learning. He received numerous awards/honors bestowed by IEEE, International Speech Communication Association, Acoustical Society of America, Asia-Pacific Signal & Information Processing Association, Microsoft, and other organizations.

His current (and past) research activities include deep learning and machine intelligence applied to big text data and to speech, image and multimodal processing, computational neuroscience and information representation, deep/recurrent/dynamic neural networks, automatic speech and speaker recognition, spoken language identification and understanding, speech-to-speech translation, machine translation, language modeling, information retrieval and data mining, web search, neural information processing, dynamic systems, machine learning and optimization, parallel and distributed computing, probabilistic graphical models, audio and acoustic signal processing, image analysis and recognition, compressive sensing, statistical signal processing, digital communication, human speech production and perception, acoustic phonetics, auditory speech processing, auditory physiology and modeling, noise robust speech processing, speech synthesis and enhancement, multimedia signal processing, and multimodal human-computer interactions.

In the general areas of audio/speech/language technology and science, machine learning, signal/information processing, and other areas of computer science, he has published over 300 refereed papers in leading journals and conferences, and authored or co-authored 5 books including the latest books on Deep Learning: Methods and Applications and on Automatic Speech Recognition: A Deep-Learning Approach (Springer). He is a Fellow of the Acoustical Society of America, a Fellow of the IEEE, and a Fellow of the International Speech Communication Association. He served on the Board of Governors of the IEEE Signal Processing Society (2008-2010), and as Editor-in-Chief for the IEEE Signal Processing Magazine (2009-2011), which earned the highest impact factor in 2010 and 2011 among all IEEE publications and for which he received the 2012 IEEE SPS Meritorious Service Award. Most recently, he served as General Chair of the IEEE ICASSP-2013, and as Editor-in-Chief for the IEEE Transactions on Audio, Speech and Language Processing (2012-2014). His technical work since 2009 (when he initiated deep learning research and technology development at Microsoft with Geoff Hinton) and the leadership in industry-scale deep learning with colleagues have created high impact in speech recognition and other areas of information processing. The work by him and the team he manages has been in use in major Microsoft speech and text/data-related products, and is recognized by IEEE SPS Best Paper Award, IEEE Outstanding Engineer Award, APSIPA Industrial Distinguished Leader Award, Microsoft Goldstar and Technology Transfer Awards.

His recent research interests and activities have been focused on deep learning and machine intelligence applied to large-scale text analysis and to speech/language/image multimodal processing, advancing his earlier work on speech analysis/recognition using deep neural networks and deep generative models.

Professional Activities and Honors/Awards

  • 2015 IEEE SPS Technical Achievement Award
  • APSIPA Industrial Distinguished Leader Award, April 2015
  • 2013 IEEE SPS Best Paper Award
  • Editor-In-Chief, IEEE Transactions Audio, Speech & Language Processing (2012-2014)
  • Editor-In-Chief, IEEE Signal Processing Magazine (2009-2011) IF: 4.9 and 6.0
  • Technology Transfer Award (on DNN development and applications), 2014
  • IEEE Outstanding Engineer Award, October, 2014
  • GoldStar Award (on deep learning), 2013
  • 2011 IEEE SPS Meritorious Service Award
  • General Chair, 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Vancouver, BC, Canada
  • Board of Governors, IEEE Signal Processing Society (Member elected, term 2008-2010)
  • Board of Governors, and VP Industrial Relations, Asian-Pacific Signal and Information Processing Association (APSIPA) (elected Sept. 2009)
  • Publications Board, IEEE Signal Processing Society (Member, 2009-2011, 2012-2014)
  • Plenary speaker: Deep Learning: From Machine Perception to Machine Cognition, The 41th IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), March 24, 2016
  • Plenary speaker: Deep Learning: Propelling Recent Rapid Advances in Artificial Intelligence, IEEE GlobalSIP Conference, Orlando, Florida, Dec. 16, 2015
  • Plenary speaker at IEEE International Symposium on Multimedia, Miami, Florida, Dec, 2015
  • Keynote speaker at NIPS Multimodal Machine Learning Workshop, Montreal, Dec. 2015
  • Panel speaker at NIPS Machine Learning for SLU Workshop, Montreal, Dec., 2015
  • Invited Speaker at Pujiang Innovation Forum, Shanghai, Oct 2015
  • Plenary speaker: Deep Generative and Discriminative Models for Speech Recognition, and Deep Learning Panel Moderator, Yandex School of Data Analysis Conference, Berlin, Oct 2015
  • Keynote speaker: IEEE ChinaSIP Conference, July 12, 2015
  • Keynote speaker: Interspeech, Sept 18, 2014
  • Keynote speaker: The 12th China National Conference on Computational Linguistics, 2013
  • Invited speaker: NIPS Workshop on Output Representation Learning, Dec 2013
  • Keynote Speaker: IEEE Odyssey Workshop, June 2012
  • Keynote Speaker: ROCLING Conf., August, 2012
  • Keynote Speaker: 2011 Workshop on ELM, Dec 2011
  • Lecturer, CMU, March 2014
  • Tutorial on Deep Learning for Speech and Language Processing: From the perspectives of machine learning and signal processing, Interspeech, Dresden, Germany, Sept 2015
  • Tutorial Lecturer on Deep Learning for Natural Language Processing: Theory and Practice at CIKM’2014 at Shanghai, China, November 2014
  • Tutorial Lecturer on Deep Learning: From speech analysis and recognition to language and multimodal processing, ICML, June 21, 2014
  • Tutorial and Overview Lecturers on “Recent Advances in Deep Learning for Speech, Vision, and Language”. IEEE MIIS Workshop, July 2014
  • Trend/Overview Lecturer on “Deep Learning for Speech and Language”, IEEE Conf. ChinaSIP, July 2014
  • Tutorial Lecturer on Deep learning for natural language processing and related applications, IEEE ICASSP, 2014
  • Tutorial Lecturer on Deep Learning for Speech and Information Processing: IEEE ICASSP, Kyoto, 2012
  • ISCA Distinguished Lecturer 2010-2011 (International Speech Comm Assoc.)
  • APSIPA Inaugural Distinguished Lecturer 2012-2013 (Asian-Pacific Sig. & Information Processing Association)
  • Tutorial Lecturer on Deep Learning: APSIPA, Xi’an, Oct 2011
  • Tutorial Lecturer, ISCA Interspeech, Portland, Sept 2012
  • Lecturer, CLSP, Johns Hopkins U., October, 2012
  • Guest Editor: IEEE Transactions on Pattern Analysis & Machine Intelligence, Special Issue, 2012
  • Editor, Computer Speech and Language (2009-2012)
  • GoldStar Awards, Microsoft Achievement Awards, MSR Tech Transfer Awards, etc. 2002-2013
  • Area Editor, IEEE Signal Processing Magazine (2006-2008)
  • General Chair, IEEE Workshop on Multimedia Signal Processing, Victoria, BC, Canada (2006)
  • Co-organizer, 2013 ICML Workshop: Deep Learning Architecture for Audio, Speech & Language Processing, Atlanta, June 2013
  • Lead Organizer, 2011 ICML Workshop: Learning Architecture, Representation & Optimization for Speech & Visual Information Processing, Bellevue, WA, July 2011
  • Co-Chair, NIPS Workshop: Speech and Language — Learning-Based Methods and Systems, Whistler, BC, Canada, 2008
  • Co-Chair, NIPS Workshop: Deep Learning for Speech Recognition and Related Applications, Whistler, BC, Canada, 2009
  • Guest Editor, IEEE Journal of Selected Topics in Signal Processing, Special Issue on Statistical Learning Methods for Speech and Language Processing, 2009
  • IEEE Signal Processing Society TC Review Committee (Member, term 2008-2009)
  • IEEE Signal Processing Society Long Range Planning & Implementation Committee (Member, term 2009-2010)
  • Member, Multimedia Signal Processing Technical Committee of the IEEE Signal Processing Society (2004-2008)
  • Member, Editorial Board, IEEE Signal Processing Letters (2007-2008)
  • Member, Editorial Board, IEEE Signal Processing Magazine (2005-2007)
  • Member, Editorial Board, J. Audio, Music, and Speech Processing (2005-present)
  • Founding Member, Education Committee, IEEE Signal Processing Society (1997-2000)
  • Member, Speech Processing Technical Committee, IEEE Signal Processing Society (1996-1999)
  • Associate Editor, IEEE Transactions on Speech and Audio Processing (2002-2005)
  • Principal Investigator, DARPA (US DoD) EARS Program, (2002-2005)
  • Technical Chair, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) (2004), Montreal, Quebec, Canada
  • Co-Guest Editor, IEEE Signal Processing Magazine, Special Issue on Speech Technology and Systems in Human-Machine Communication (Sept 2005)
  • Co-Guest Editor, IEEE Trans. on Computers, Special Issue on Emergent Systems, Algorithms and Architectures for Speech-based Human-Machine Interaction (2006)
  • Member, IEEE Signal Processing Society Technical Directions Committee (2003-2005)
  • Member, IEEE International Conference on Multimedia and Expo Steering Committee (2004-2006)
  • Keynote speaker, IEEE 5th Workshop on Multimedia Signal Processing (IEEE Signal Processing Society), St. Thomas, US Virgin Islands (December 2002)
  • Organizer and speaker, AAAS (American Association for Advancement of Science) Symposium on “Scientific Problems Facing Speech Recognition Today”, Seattle, 2004
  • Gold Star Award, Microsoft Corp, 2002, 2013
  • Invited Lecturer, NATO Advanced Study Institute
  • Invited Lecturer, European Speech Communication (ESCA) Tutorial and Research Workshops
  • Bell Canada Research Award, 1999
  • National Defense of Canada Research Award, 1997, 1999
  • Center of Information and Telecommunication Ontario Research Award, 1998
  • NSERC Industrial Oriented Research Award, 1991, 1994, 1997
  • NSERC Collaborative Research and Development Award, 1993, 1996, 1998
  • Nortel Technology Research Award, 1991, 1993, 1996, 1998
  • NSERC Strategic Grant Award, 1995
  • Ontario Information Technology Research Center of Excellence Grant Award, 1994, 1995
  • Ontario University-Industry Research Incentive Award, 1991, 1993
  • Natural Science and Engineering Research Council (NSERC, Canada) Presidential Award, 1991
  • The Sixth Jerzy E. Rose Award, 1986, for original and significant research in auditory science
  • The First Guo Mo-Ruo Award, for top academic ranking graduates in science and engineering at the University of Science and Technology of China
  • Fellow, The Acoustical Society of America (The American Institute of Physics) (elected Dec. 2003)
  • Fellow, The IEEE (elected Dec. 2004)
  • Fellow, The ISCA (elected Aug. 2011)

Books

Book Chapters

  • Li Deng and Navdeep Jaitly, Chapter 1.2 Deep Discriminative and Generative Models for Speech Pattern Recognition, in Handbook of Pattern Recognition and Computer Vision (Ed. C.H. Chen), pp. 27-52, World Scientific, January 2016.
  • Jinyu Li, Li Deng, Reinhold Haeb-Umbach, and Yifan Gong, Compensation with Prior Knowledge, in Robust Automatic Speech Recognition: A Bridge to Practical Applications, Elsevier, October 2015.
  • Li Deng, Connecting Deep Learning Features to Log-Linear Models, MIT Press, August 2015.
  • Li Deng and Roberto Togneri, Chapter 6: Deep Dynamic Models for Learning Hidden Representations of Speech Features, pp. 153-196, Springer, December 2014.
  • Li Deng and Dong Yu, Chapter 13: Recurrent Neural Networks and Related Models, in Automatic Speech Recognition — A Deep Learning Approach, Springer, October 2014.
  • Dong Yu and Li Deng, Chapter 8: Deep Neural Network Sequence-Discriminative Training , in Automatic Speech Recognition — A Deep Learning Approach, Springer, October 2014.
  • Li Deng and Dong Yu, Chapter 2: Gaussian Mixture Models, in Automatic Speech Recognition — A Deep Learning Approach, Springer, October 2014.
  • Li Deng and Dong Yu, Chapter 3: Hidden Markov Models and the Variants, in Automatic Speech Recognition — A Deep Learning Approach, Springer, September 2014.
  • Dong Yu and Li Deng, Chapter 15: Summary and Future Directions, in Automatic Speech Recognition — A Deep Learning Approach, Springer, September 2014.
  • Li Deng and Dong Yu, Deep Learning for Signal and Information Processing, NOW Publishers, 2013.
  • Li Deng, Front-End, Back-End, and Hybrid Techniques to Noise-Robust Speech Recognition, in D. Kolossa and R. Hab-Umbach (eds.) Robust Speech Recognition of Uncertain Data, pp. 67-99, Springer Verlag, 2011.
  • Gokhan Tur and Li Deng, Intent Determination and Spoken Utterance Classification, in Chapter 4, Tur and De Mori (eds) Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, , pp. 81-104, Wiley, 2011.
  • Yeyi Wang, L. Deng, and A. Acero, Semantic Frame Based Spoken Language Understanding, in Chapter 3, Tur and De Mori (eds) Spoken Language Understanding: Systems for Extracting Semantic Information from Speech, , pp. 35-80, Wiley, 2011.
  • Xuedong Huang and Li Deng, An Overview of Modern Speech Recognition , in Handbook of Natural Language Processing, Second Edition, Chapter 15 (ISBN: 1420085921), pp. 339-366, Chapman & Hall/CRC, 2010.
  • Li Deng, KS Wang, and R. Guido, A Semantic and Detection-based Approach to Speech and Language Processing, in Phillip Sheu; Heather Yu; C V Ramamoorthy; Arvind K Joshi; Lotfi A Zadeh (eds) Semantic Computing, pp. 49-68, Wiley, 2010.
  • Dong Yu and Li Deng, Speech-Centric Multimodal User Interface Design in Mobile Technology, in Chapter XVIII in Jo Lumsden (Ed.) in Handbook of Research on User Interface Design and Evaluation for Mobile Technology, IGI Global, January 2008.
  • Li Deng and Jianwu Dang, Speech Analysis: The Production-Perception Perspective, in Advances in Chinese Spoken Language Processing, pp. 2-32, World Scientific Publishing, 2007.
  • Li Deng and H. Sheikhzadeh, Use of Temporal Codes Computed from a Cochlear Model for Speech Recognition, in Chapter 15, S. Greenberg and W. Ainsworth (eds.) Listening to Speech: An Auditory Perspective, pp. 237-256, Lawrence Erlbaum Associates, Inc., 2006.
  • A. Avendano, Li Deng, H. Hermansky, and B. Gold, The Analysis and Representation of Speech, in Speech Processing in the Auditory System; Chapter 2; S. Greenberg, W. Ainsworth, A. Popper, and R. Fay (eds.), ISBN: 978-0-387-00590-4 , Springer Verlag, 2004.
  • Li Deng, Switching Dynamic System Models for Speech Articulation and Acoustics, in Mathematical Foundations of Speech and Language Processing, vol. 138, pp. 115 – 134, Springer Verlag, 2003.
  • Li Deng, Computational Models for Auditory Speech Processing, in Computational Models of Speech Pattern Processing, (NATO ASI Series), pp. 67-77, Springer Verlag, 1999.
  • Li Deng, Computational Models for Speech Production, in Computational Models of Speech Pattern Processing, (NATO ASI Series), pp. 199-213, Springer Verlag, 1999.
  • Li Deng, Articulatory Features and Associated Production Models in Statistical Speech Recognition, in Computational Models of Speech Pattern Processing, (NATO ASI Series), pp. 214-224, Springer Verlag, 1999.
  • Li Deng, A dynamic, feature-based approach to speech modeling and recognition, in in S. Furui, F. Juang (eds.) Automatic Speech Recognition and Understanding , pp. 107-114, Institute of Electrical and Electronics Engineers, Inc., 1997.
  • Don Sun and Li Deng, Nonstationary-State Hidden Markov Models for Speech Recognition, in Chapter 8, S. Levinson and L. Shepp (eds.): Image and Speech Models, Springer Verlag, 1995.
  • D. Zhang, Li Deng, and M. Elmasry, Pipelined Neural Network Architecture For Speech Recognition, in VLSI Artificial Neural Networks Engineering, pp. 297-315, Kluwer Academic , 1994.
  • K. Hassanein, Li Deng, and M. Elmasry, Neural Predictive Hidden Markov Model Architecture For Speech And Speaker Recognition, in in VLSI Artificial Neural Networks Engineering, pp. 316-336, Kluwer Academic , 1994.
  • Li Deng, K. Hassanein, and M. Elmasry, Neural-Network Architecture For Linear And Nonlinear Predictive Hidden Markov Models: Application To Speech Recognition, in in B. H. Juang, S. Y. Kung, and C. A. Kamm, (eds.) Neural Networks for Signal Processing, Institute of Electrical and Electronics Engineers, Inc., 1991.

Journal/Magazine Editorials

2016

  • Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward, Deep Sentence Embedding Using Long Short-Term Memory Networks: Analysis and Application to Information Retrieval , in IEEE/ACM Transactions on Audio, Speech, and Language Processing, March 2016.
  • Li Deng, Industrial Technology Advances: Deep learning — from speech recognition to language and multimodal processing, in APSIPA Transactions on Signal and Information Processing (Cambridge University Press), February 2016.

2015

  • Xiujun Li, Lihong Li, Jianfeng Gao, Xiaodong He, Jianshu Chen, Li Deng, and Ji He, Recurrent Reinforcement Learning: A Joint Training Approach, in ArXiv, September 2015.
  • Hao Fang, Saurabh Gupta, Forrest Iandola, Rupesh Srivastava, Li Deng, Piotr Dollar, Jianfeng Gao, Xiaodong He, Margaret Mitchell, John Platt, Lawrence Zitnick, and Geoffrey Zweig, From Captions to Visual Concepts and Back, in The proceedings of CVPR, IEEE – Institute of Electrical and Electronics Engineers, June 2015.
  • Xiaodong Liu, Jianfeng Gao, Xiaodong He, Li Deng, Kevin Duh, and Ye-Yi Wang, Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval, in NAACL, NAACL, May 2015.
  • Grégoire Mesnil, Yann Dauphin, Kaisheng Yao, Yoshua Bengio, Li Deng, Dilek Hakkani-Tur, Xiaodong He, Larry Heck, Gokhan Tur, Dong Yu, and Geoffrey Zweig, Using Recurrent Neural Networks for Slot Filling in Spoken Language Understanding, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, IEEE – Institute of Electrical and Electronics Engineers, March 2015.
  • Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward, Deep Sentence Embedding Using the Long Short Term Memory Network: Analysis and Application to Information Retrieval, in arXiv:1502.06922, arXiv, February 2015.

2014

  • Li Deng, Keeping Up the Momentum of Innovations, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, December 2014.
  • Ossama Abdel-Hamid, Abdel-rahman Mohamed, Hui Jiang, Li Deng, Gerald Penn, and Dong Yu, Convolutional Neural Networks for Speech Recognition, in IEEE/ACM TRANSACTIONS ON AUDIO, SPEECH, AND LANGUAGE PROCESSING, IEEE – Institute of Electrical and Electronics Engineers, October 2014.
  • O. Abdel-Hamid, A. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu, Convolutional Neural Networks for Speech Recognition, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 10, pp. 1533-1545, October 2014.
  • Jinyu Li, Li Deng, Yifan Gong, and Reinhold Haeb-Umbach, An Overview of Noise-Robust Automatic Speech Recognition, in IEEE/ACM Transactions on Audio, Speech, and Language Processing, vol. 22, no. 4, pp. 745 – 777 , IEEE – Institute of Electrical and Electronics Engineers, April 2014.
  • Li Deng, A Tutorial Survey of Architectures, Algorithms, and Applications for Deep Learning , in APSIPA Transactions on Signal and Information Processing, Cambridge University Press, 2014.

2013

  • Stephen J. Wright, Dimitri Kanevsky, Li Deng, Xiaodong He, Georg Heigold, and Haizhou Li, Optimization Algorithms and Applications for Speech and Language Processing, in IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 11, pp. 2231-2243, November 2013.
  • Zhenhua Ling, Li Deng, and Dong Yu, Modeling Spectral Envelopes Using Restricted Boltzmann Machines and Deep Belief Networks for Statistical Parametric Speech Synthesis, in IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 10, pp. 2129-2139, October 2013.
  • Brian Hutchinson, Li Deng, and Dong Yu, Tensor Deep Stacking Networks, in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 35, no. 8, IEEE, August 2013.
  • Samy Bengio, Li Deng, Hugo Larochelle, Honglak Lee, and Ruslan Salakhtdinov, Guest Editors’ Introduction: Special Section Learning Deep Architectures, in IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 35, no. 8, August 2013.
  • Xiaodong He and Li Deng, Speech-Centric Information Processing: An Optimization-Oriented Approach, in Proceedings of the IEEE, IEEE, 31 May 2013.
  • Li Deng and Xiao Li, Machine Learning Paradigms for Speech Recognition: An Overview, in IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 5, pp. 1060-1089, May 2013.
  • Sabato Marco Siniscalchi, Dong Yu, Li Deng, and Chin-Hui Lee, Exploiting Deep Neural Networks for Detection-Based Speech Recognition, in Neurocomputing, Elsevier, April 2013.
  • Sabato Marco Siniscalchi, Dong Yu, Li Deng, and Chin-hui Lee, Speech Recognition Using Long-Span Temporal Patterns in a Deep Network Model, in IEEE Signal Processing Letters, vol. 20, no. 3, pp. 201-204, March 2013.
  • Kaisheng Yao, Dong Yu, Li Deng, and Yifan Gong, A fast maximum likelihood nonlinear feature transformation method for GMM-HMM speaker adaptation, in Neurocomputing, 2013.
  • Dong Yu, Li Deng, and Frank Seide, The Deep Tensor Neural Network with Applications to Large Vocabulary Speech Recognition, in IEEE Transactions on Audio, Speech, and Language Processing, vol. 21, no. 2, pp. 388-396, IEEE, 2013.

2012

  • O. Vinyals, Y. Jia, Li Deng, and Trevor Darrell, Learning with Recursive Perceptual Representations, in Neural Information Processing Systems (NIPS), vol. 15, December 2012.
  • Li Deng, The MNIST database of handwritten digit images for machine learning research, in IEEE Signal Processing Magazine, no. 141-142, November 2012.
  • Geoffrey Hinton, Li Deng, Dong Yu, Abdel-rahman Mohamed, Navdeep Jaitly, Andrew Senior, Vincent Vanhoucke, Patrick Nguyen, Tara Sainath George Dahl, and Brian Kingsbury, Deep Neural Networks for Acoustic Modeling in Speech Recognition, in IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, November 2012.
  • Sadaoki Furui, Li Deng, Hermann Ney, and and Keiichi Tokuda Mark Gales, Fundamental Technologies in Modern Speech Recognition, in IEEE Signal Processing Magazine, November 2012.
  • Xiaodong He and Li Deng, Maximum Expected BLEU Training of Phrase and Lexicon Translation Models , in Proceedings of ACL, Association for Computational Linguistics, July 2012.
  • Dong Yu and Li Deng, Efficient and Effective Algorithms for Training Single-Hidden-Layer Neural Networks, in Pattern Recognition Letters, Elsevier, 2012.
  • Li Deng, Editorial: Riding the Tidal Wave of Human-Centric Information Processing, in IEEE Trans. Audio Speech & Language Proc., January 2012.
  • Li Deng, Three Classes of Deep Learning Architectures and Their Applications: A Tutorial Survey, in APSIPA Transactions on Signal and Information Processing, 2012.
  • George Dahl, Dong Yu, Li Deng, and Alex Acero, Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition, in IEEE Transactions on Audio, Speech, and Language Processing (receiving 2013 IEEE SPS Best Paper Award) , vol. 20, no. 1, pp. 30-42, January 2012.

2011

  • Dong Yu, Jinyu Li, and Li Deng, Calibration of confidence measures in speech recognition, in IEEE Transactions on Audio, Speech, and Language Processing, IEEE SPS, November 2011.
  • Li Deng, Shining Bright: The Golden Era of Signal Processing, in IEEE Signal Processing Magazine, November 2011.
  • Xiaodong He and Li Deng, Speech Recognition, Machine Translation, and Speech Translation – A Unified Discriminative Learning Paradigm, in IEEE Signal Processing Magazine, September 2011.
  • Z. Jane Wang and Li Deng, Democratizing Signal Processing, in IEEE Signal Processing Magazine, March 2011.
  • Li Deng, Innovating Our Magazine in the Global, Interconnected Information Age, in IEEE Signal Processing Magazine, vol. 28, no. 1, January 2011.
  • Dong Yu and Li Deng, Deep Learning and Its Applications to Signal and Information Processing , in IEEE Signal Processing Magazine, IEEE, January 2011.
  • Dong Yu, Jinyu Li, and Li Deng, Calibration of confidence measures in speech recognition, in IEEE Transactions on Audio, Speech and Language Processing, 2011.

2010

  • Dong Yu, Shizhen Wang, and Li Deng, Sequential Labeling Using Deep-Structured Conditional Random Fields, in IEEE JOURNAL OF SELECTED TOPICS IN SIGNAL PROCESSING, IEEE, December 2010.
  • Xiaodong He, Li Deng, Roland Kuhn, Helen Meng, and Samy Bengio, Introduction to the Issue on Statistical Learning Methods for Speech and Language Processing, in IEEE Journal of Selected Topics in Signal Processing, IEEE, December 2010.
  • Lin Xiao and Li Deng, A Geometric Perspective of Large-Margin Training of Gaussian Models, in IEEE Signal Processing Magazine, vol. 27, no. 6, pp. 118-123, IEEE, November 2010.
  • Li Deng, Impact of Signal Processing and of Our Work, in IEEE Signal Processing Magazine, vol. 27, no. 6, November 2010.
  • Li Deng, New Focus, New Challenge, in IEEE Signal Processing Magazine, vol. 27, no. 2, March 2010.

2009

  • Dong Yu, Li Deng, and Alex Acero, Using continuous features in the maximum entropy model, in Pattern Recognition Letters, vol. 30, no. 8, pp. 1295-1300, Elsevier , October 2009.
  • Dong Yu, Li Deng, Yifan Gong, and Alex Acero, A Novel Framework and Training Algorithm for Variable-Parameter Hidden Markov Models, in IEEE Transactions on Audio, Speech and Language Processing, vol. 17, no. 7, pp. 1348-1360, IEEE, September 2009.
  • J. Baker, Li Deng, S. Khudanpur, C.-H. Lee, J. Glass, and N. Morgan, Updated MINDS Report on Speech Recognition and Understanding, in IEEE Signal Processing Magazine, vol. 26, no. 4, July 2009.
  • Dong Yu and Li Deng, Solving nonlinear estimation problems using Splines , in IEEE Signal Processing Magazine, vol. 26, no. 4, pp. 86-90, IEEE, July 2009.
  • J. Baker, Li Deng, Jim Glass, S. Khudanpur, C.-H. Lee, N. Morgan, and D. O’Shgughnessy, Research Developments and Directions in Speech Recognition and Understanding, Part 1, in IEEE Signal Processing Magazine, vol. 26, no. 3, pp. 75-80, May 2009.
  • Li Deng, Curiosity in Science and Technology, in IEEE Signal Processing Magazine, vol. 26, no. 3, pp. 2-4, May 2009.
  • Dong Yu and Li Deng, Teach-Ware: Signal Processing Resources at Connexions, in IEEE Signal Processing Magazine, Institute of Electrical and Electronics Engineers, Inc., March 2009.
  • Jinyu Li, Dong Yu, Li Deng, Yifan Gong, and Alex Acero, A unified framework of HMM adaptation with joint compensation of additive and convolutive distortions, in Computer Speech and Language, vol. 23, pp. 389-405, Elsevier , 2009.
  • Li Deng, Embracing a New Golden Age of Signal Processing, in IEEE Signal Processing Magazine, January 2009.
  • Dong Yu, Balakrishnan Varadarajan, Li Deng, and Alex Acero, Active Learning and Semi-supervised Learning for Speech Recognition: A Unified Framework using the Global Entropy Reduction Maximization Criterion, in Computer Speech and Language – Special Issue on Emergent Artificial Intelligence Approaches for Pattern Recognition in Speech and Language Processing , Elsevier , 2009.

2008

  • Dong Yu, Li Deng, Xiaodong He, and Alex Acero, Large-Margin Minimum Classification Error Training: A Theoretical Risk Minimization Perspective, in Computer Speech and Language, vol. 22, no. 4, pp. 415-429, Elsevier , October 2008.
  • Xiaodong He, Li Deng, and Wu Chou, Discriminative Learning in Sequential Pattern Recognition, in IEEE Signal Processing Magazine, vol. 25, no. 5, pp. 14-36, Institute of Electrical and Electronics Engineers, Inc., September 2008.
  • Dong Yu, Li Deng, Jasha Droppo, Jian Wu, Yifan Gong, and Alex Acero, Robust speech recognition using cepstral minimum-mean-square-error noise suppressor, in IEEE Trans. Audio, Speech, and Language Processing, vol. 16, no. 5, Institute of Electrical and Electronics Engineers, Inc., July 2008.
  • Li Deng, Expanding the Scope of Signal Processing, in IEEE Signal Processing Magazine, vol. 25, no. 3, pp. 2-4, May 2008.
  • Sibel Yaman, Li Deng, Dong Yu, Ye-Yi Wang, and Alex Acero, An integrative and discriminative technique for spoken utterance classification, in IEEE Trans. Audio, Speech, and Language Processing, vol. 16, no. 6, pp. 1207-1214, 2008.

2007

  • Rodrigo Guido, Li Deng, and Shoji Makino, Guest Editors’ Introduction: Special Section on Emergent Systems, Algorithms, and Architectures for Speech-Based Human-Machine Interaction, in IEEE Transactions on Computers, vol. 56, no. 9, pp. 1153-1155, September 2007.
  • Li Deng, Write Feature Articles with a Lasting Impact, in IEEE Signal Processing Magazine, vol. 24, no. 2, March 2007.
  • Xiaodong He and Li Deng, A new look at discriminative learning for hidden Markov models, in Pattern Recognition Letters, vol. 28, pp. 1285-1294, 2007.
  • Li Deng, Hagai Attias, Leo Lee, and Alex Acero, Adaptive Kalman smoothing for tracking vocal tract resonances using a continuous-valued hidden dynamic model, in IEEE Transactions on audio, Speech and Language Processing, vol. 15, no. 1, pp. 13-23, Institute of Electrical and Electronics Engineers, Inc., January 2007.
  • Dong Yu, Li Deng, and Alex Acero, Speaker-adaptive learning of resonance targets in a hidden trajectory model of speech coarticulation, in Computer Speech and Language, vol. 27, pp. 72-87, Elsevier , 2007.

2006

  • Li Deng, Dong Yu, and Alex Acero, Structured Speech Modeling, in IEEE Trans. on Audio, Speech and Language Processing, vol. 14, no. 5, pp. 1492-1504, Institute of Electrical and Electronics Engineers, Inc., September 2006.
  • Dong Yu, Li Deng, and Alex Acero, A Lattice Search Technique for a Long-Contextual-Span Hidden Trajectory Model of Speech, in Speech Communication, Elsevier , September 2006.
  • I. Bazzi, Li Deng, and Alex Acero, Tracking Vocal Tract Resonances Using a Quantized Nonlinear Function Embedded in a Temporal Constraint, in IEEE Trans. on Audio, Speech and Language Processing, vol. 14, no. 2, pp. 425-434, March 2006.
  • Roberto Togneri and Li Deng, A state-space model with neural-network prediction for recovering vocal tract resonances in fluent speech from Mel-cepstral coefficients, in Speech Communication, vol. 48, pp. 971-988, 2006.
  • Li Deng, Dong Yu, and Alex Acero, A Bidirectional Target Filtering Model of Speech Coarticulation: two-stage Implementation for Phonetic Recognition, in IEEE Transactions on Audio and Speech Processing, vol. 14, no. 1, pp. 256-265, IEEE, January 2006.

2005

  • Li Deng and Dong Yu, A Speech-Centric Perspective for Human-Computer Interface – A Case Study, in Journal of VLSI Signal Processing Systems (Special Issue on Multimedia Signal Processing), Springer Verlag, November 2005.
  • Li Deng, K. Wang, and Wu Chou, Speech Technology and Systems in Human-Machine Communication, in IEEE Signal Processing Magazine, vol. 22, no. 5, pp. 12-14, September 2005.
  • Li Deng, J. Wu, Jasha Droppo, and Alex Acero, Analysis and Comparison of Two Speech Feature Extraction/Compensation Algorithms, in IEEE Signal Processing Letters, vol. 12, no. 6, pp. 477–480, Institute of Electrical and Electronics Engineers, Inc., June 2005.
  • Li Deng, Jian Wu, Jasha Droppo, and Alex Acero, Dynamic Compensation of HMM Variances Using the Feature Enhancement Uncertainty Computed From a Parametric Model of Speech Distortion, in IEEE Transactions on Speech and Audio Processing, vol. 13, no. 3, pp. 412–421, Institute of Electrical and Electronics Engineers, Inc., May 2005.
  • Ye-Yi Wang, Li Deng, and Alex Acero, Spoken Language Understanding — An Introduction to the Statistical Framework, in IEEE Signal Processing Magazine, vol. 22, no. 5, pp. 16-31, Institute of Electrical and Electronics Engineers, Inc., 2005.

2004

  • Li Deng and Xuedong Huang, Forum: Author Response to ‘For Voice Interfaces, Hold the SALT’, in Communications of the ACM. Vol. 47, No. 7, July 2004, pp. 11-13, July 2004.
  • Li Deng, Jasha Droppo, and Alex Acero, Estimating cepstrum of speech under the presence of noise using a joint prior of static and dynamic features, in IEEE Transactions on Speech and Audio Processing, vol. 12, no. 3, pp. 218–233, Institute of Electrical and Electronics Engineers, Inc., May 2004.
  • Li Deng, Jasha Droppo, and Alex Acero, Enhancement of log Mel power spectra of speech using a phase-sensitive model of the acoustic environment and sequential estimation of the corrupting noise, in IEEE Transactions on Speech and Audio Processing, vol. 12, no. 2, pp. 133–143, Institute of Electrical and Electronics Engineers, Inc., March 2004.
  • Li Deng and Xuedong Huang, Challenges in Adopting Speech Recognition, in Communications of the ACM, vol. 47, no. 1, pp. 11-13, January 2004.
  • J. Ma and Li Deng, Target-Directed Mixture Dynamic Models for Spontaneous Speech Recognition, in IEEE Trans. on Speech and Audio Processing, vol. 12, no. 1, pp. 47-58, January 2004.
  • Li Deng, Ye-Yi Wang, Kuansan Wang, Alex Acero, Hsiao Hon, Jasha Droppo, C. Boulis, Derek Jacoby, Milind Mahajan, Ciprian Chelba, and Xuedong Huang, Speech and language processing for multimodal human-computer interaction (Invited Article) , in Journal of VLSI Signal Processing Systems (Special issue on Real-World Speech Processing), vol. 36, no. 2-3, pp. 161 – 187, Kluwer Academic , 2004.
  • J. Ma and Li Deng, A Mixed-Level Switching Dynamic System for Continuous Speech Recognition, in Computer, Speech and Language, vol. 18, pp. 49-65, 2004.

2003

  • R. Togneri and Li Deng, Joint State and Parameter Estimation for a Target-Directed Nonlinear Dynamic System Model, in IEEE Trans. on Signal Processing, vol. 51, no. 12, pp. 3061-3070, December 2003.
  • Li Deng, Jasha Droppo, and Alex Acero, Recursive estimation of nonstationary noise using iterative stochastic approximation for robust speech recognition, in IEEE Transactions on Speech and Audio Processing, vol. 11, no. 6, pp. 568–580, Institute of Electrical and Electronics Engineers, Inc., November 2003.
  • J. Ma and Li Deng, Efficient Decoding Strategies for Conversational Speech Recognition Using a Constrained Nonlinear State-Space Model, in IEEE Trans. on Speech and Audio Processing, vol. 11, no. 6, pp. 590-602, November 2003.
  • J. Xin, Y. Qi, and Li Deng, Time Domain Computation of a Nonlinear Nonlocal Cochlear Model with Applications to Multitone Interactions in Hearing, in Communications in Mathematical Sciences, vol. 1, no. 2, pp. 211-227, 2003.

2002

  • M. Naito, Li Deng, and Y. Sagisaka, Speaker clustering for speech recognition using vocal-tract parameters, in Speech Communication, vol. 36, no. 3-4, pp. 305-315, March 2002.
  • H. Sameti and Li Deng, Nonstationary-state hidden Markov model representation of speech signals for speech enhancement, in Signal Processing, vol. 82, pp. 205-227, 2002.
  • Jiping Sun and Li Deng, An overlapping-feature based phonological model incorporating linguistic constraints: Applications to speech recognition, in Journal of the Acoustical Society of America, vol. 111, no. 2, pp. 1086-1101, 2002.
  • Hui Jiang and Li Deng, A robust compensation strategy against extraneous acoustic variations in spontaneous speech recognition, in IEEE Transactions on Speech & Audio Processing, vol. 10, no. 1, pp. 9-17, January 2002.
  • Li Deng, Kuansan Wang, Alex Acero, Hsiao-Wuen Hon, Jasha Droppo, Constantinos Boulis, Ye-Yi Wang, Derek Jacoby, Milind Mahajan, Ciprian Chelba, and Xuedong D. Huang, Distributed Speech Processing in MiPad’s Multimodal User Interface, in IEEE TRANSACTIONS ON SPEECH AND AUDIO PROCESSING, vol. 10, no. 8, pp. 605-619, Institute of Electrical and Electronics Engineers, Inc., 2002.

2001

  • R. Chengalvarayan and Li Deng, A Maximum a Posteriori Approach to Speaker Adaptation Using the Trended Hidden Markov model, in IEEE Trans. on Speech and Audio Processing. Volume: 9 Issue: 5, July 2001.
  • R. Togneri, J. Ma, and Li Deng, Parameter estimation of a target-directed dynamic system model with switching states, in Signal Processing, vol. 81, no. 5, pp. 975-987, 2001.
  • H. Jiang and Li Deng, A Bayesian approach to speaker verification, in IEEE Trans. Speech & Audio Proc., 2001.

2000

  • Li Deng and J. Ma, Spontaneous Speech Recognition Using a Statistical Coarticulatory Model for the Vocal Tract Resonance Dynamics, in Journal of the Acoustical Society of America, 2000.
  • Jeff Ma and Li Deng, A path-stack algorithm for optimizing dynamic regimes in a statistical hidden dynamic model of speech, in Computer Speech and Langu, vol. 14, pp. 101-104 , 2000.
  • M. Naito, Li Deng, and Y. Sagisaka, Speaker normalization for speech recognition using model-based vocal-tract parameters, in Transactions of Japan Institute of Electronics, Information, and Communication Engineers (IEICE), vol. J83-D-II , no. 11, pp. 2360-2369, 2000.

1999

  • X. Shen and Li Deng, A Dynamic System Approach to Speech Enhancement Using the H-inf Filtering Algorithm,, in IEEE Trans. on Speech and Audio Processing, vol. 7, pp. 391-399, July 1999.
  • J. Sun and Li Deng, Use of high-level linguistic constraints for constructing feature-based phonological model in speech recognition, in Journal of Intelligent Information Processing Systems, pp. 269-276, 1999.
  • H. Sheikhzadeh and Li Deng, A layered neural network interfaced with a cochlear model for the study of speech encoding in the auditory syst, in Computer Speech and Language, vol. 13, pp. 39-64, 1999.

1998

  • R. Chengalvarayan and Li Deng, Speech Trajectory Discrimination using the Minimum Classification Error Learning, in IEEE Trans. on Speech and Audio Processing, vol. 6, no. 6, pp. 505-515, November 1998.
  • H. Sameti, H. Sheikhzadeh, Li Deng, and R. Brennan, HMM-based Strategies for Enhancement of Speech Signals Embedded in Nonstationary Noise, in IEEE Trans. on Speech and Audio Processing, vol. 6, no. 5, pp. 445-455, January 1998.
  • Li Deng, A dynamic, feature-based approach to the interface between phonology and phonetics for speech modeling and recognition, in Speech Communication, vol. 24, no. 4, pp. 299-323, 1998.
  • Li Deng, Locus equation and hidden parameters of speech, in Journal of Behavioral and Brain Sciences, vol. 21, no. 2, pp. 263-264, 1998.
  • H. Sheikhzadeh and Li Deng, Speech Analysis and Recognition using Interval Statistics Generated from a Composite Auditory Model, in IEEE Trans. on Speech and Audio Processing, vol. 6, no. 1, pp. 50-54, IEEE, January 1998.

1997

  • Li Deng, G. Ramsay, and D. Sun, Production models as a structural basis for automatic speech recognition,” Speech Communication (special issue on speech production modeling), in Speech Communication, vol. 22, no. 2, pp. 93-112, August 1997.
  • Xuemin Shen and Li Deng, Game theory approach to H-infinity filter design, in IEEE Transactions on Signal Processing, vol. 45, no. 4, pp. 1092-1095, April 1997.
  • Rathinavelu Chengalvarayan and Li Deng, HMM-based speech recognition using state-dependent, discriminatively derived transforms on Mel-warped DFT features, in IEEE Transactions on Speech and Audio Processing, pp. 243-256, 1997.
  • Li Deng, Autosegmental representation of phonological units of speech and its phonetic interface, in Speech Communication, vol. 23, no. 3, pp. 211-222, 1997.
  • X. Shen and Li Deng, Maximum likelihood in statistical estimation of dynamical systems: Decomposition algorithm and simulation results, in Signal Processing, vol. 57, no. 1, pp. 65-79, 1997.
  • Li Deng and M. Aksmanovic, Speaker-independent phonetic classification using hidden Markov models with state-conditioned mixtures of trend functions, in IEEE Transactions on Speech and Audio Processing, vol. 5, no. 4, pp. 319-324, 1997.
  • C. Rathinavalu and Li Deng, Use of generalized dynamic feature parameters for speech recognition, in IEEE Transactions on Speech and Audio Processing, pp. 232-242, 1997.
  • C. Rathinavalu and Li Deng, Construction of state-dependent dynamic parameters by maximum likelihood: Applications to speech recognition, in Signal Processing, vol. 55, no. 2, pp. 149-165, 1997.

1996

  • Xuemin Shen and Li Deng, Decomposition solution of H-infinity filter gain in singularly perturbed systems, in Signal Processing, vol. 5, no. 4, pp. 319-324, 1996.
  • Li Deng, Transiems as dynamically-defined, sub-phonemic units of speech: A computational model, in Signal Processing, vol. 49, no. 1, pp. 25-35, 1996.
  • Li Deng and H. Sameti, Transitional speech units and their representation by the regressive Markov states: Applications to speech recognition, in IEEE Transactions on Speech and Audio Processing, vol. 4, no. 4, pp. 301-306, 1996.

1995

  • Li Deng and C. Rathinavalu, A Markov model containing state-conditioned second-order nonstationarity: Application to speech recognition, in Computer Speech and Language, vol. 9, no. 1, pp. 63-86, 1995.
  • G. Ramsay and Li Deng, Tracking non-stationary targets using a dynamical system with Markov-modulated parameters, in IEEE Signal Processing Letters, vol. 2, no. 9, pp. 172-175, 1995.

1994

  • Li Deng, K. Hassanein, and M. Elmasry, Analysis of correlation structure for a neural predictive model with application to speech recognition, in Neural Networks, vol. 7, no. 2, pp. 331-339, April 1994.
  • H. Sheikhzadeh and Li Deng, Waveform-based speech recognition using hidden filter models: Parameter selection and sensitivity to power normalization, in IEEE Transactions on Speech and Audio Processing, vol. 2, no. 1, pp. 80-91, 1994.
  • Don Sun and Li Deng, State-dependent time warping in the trended hidden Markov model, in Signal Processing, vol. 39, no. 1, pp. 263-275, 1994.
  • Li Deng, Integrated optimization of dynamic feature parameters for hidden Markov modeling of speech, in IEEE Signal Processing Letters, vol. 1, no. 4, pp. 66-69, 1994.
  • Li Deng, M. Aksmanovic, D. Sun, and Jeff Wu, Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states, in IEEE Transactions on Speech and Audio Processing, vol. 2, no. 4, pp. 507-520, 1994.
  • Li Deng and D. Sun, A statistical approach to automatic speech recognition using the atomic speech units constructed from overlapping articulatory features, in Journal of the Acoustical Society of America, vol. 85, no. 5, pp. 2702-2719, 1994.
  • Li Deng, A statistical model for formant-transition microsegments of speech incorporating locus equations, in Signal Processing, vol. 37, no. 1, pp. 121-128, 1994.
  • D. Zhang, Li Deng, and M. Elmasry, Pipelined architectures for neural-network-based speech recognition, in Neural, Parallel & Scientific Computations, vol. 2, no. 1, pp. 81-92, 1994.
  • Li Deng and D. Braam, Context-dependent Markov model structured by locus equations: Application to phonetic classification, in Journal of the Acoustical Society of America, vol. 96, no. 4, pp. 2008-2025, 1994.

1993

  • Li Deng and Jon Mark, Parameter estimation of Markov modulated Poisson processes as a telecommunication traffic model via the EM algorithm with time discretization, in Telecommunication Systems, vol. 1, no. 3, pp. 321-338, 1993.
  • Li Deng and I. Kheirallah, Numerical property and efficient solution of a nonlinear transmission-line model for basilar-membrane wave motions, in Signal Processing, vol. 33, no. 3, pp. 269-286, 1993.
  • Li Deng, A stochastic model of speech incorporating hierarchical nonstationarity, in IEEE Transactions on Speech and Audio Processing, vol. 1, no. 4, pp. 471-475, 1993.
  • K. Erler and Li Deng, Hidden Markov model representation of quantized articulatory features for speech recognition, in Computer Speech and Language, vol. 7, no. 3, pp. 265-282, 1993.
  • Li Deng and I. Kheirallah, Dynamic formant tracking of noisy speech using temporal analysis on outputs from a nonlinear cochlear model, in IEEE Transactions on Biomedical Engineering, vol. 40, no. 5, pp. 456-467, 1993.

1992

  • Li Deng, P. Kenny, M Lennig, and P. Mermelstein, Modeling acoustic transitions in speech by state-interpolation hidden Markov models, in IEEE Transactions on Signal Processing, vol. 40, no. 2, pp. 265-272, 1992.
  • Li Deng, Processing of acoustic signals in a cochlear model incorporating laterally coupled suppressive elements, in Neural Networks, vol. 5, no. 1, pp. 19-34, 1992.
  • Li Deng and K. Erler, Structural design of a hidden Markov model based speech recognizer using multi-valued phonetic features: Comparison with segmental speech units, in Journal of the Acoustical Society of America, vol. 92, no. 6, pp. 3058-3067, 1992.
  • Li Deng, A generalized hidden Markov model with state-conditioned trend functions of time for the speech signal, in Signal Processing, vol. 27, no. 1, pp. 65-78, 1992.

1991

  • Li Deng, Hierarchical non-stationarity in a class of doubly stochastic time series models with application to speech recognition, in Canadian Acoustics, vol. 19, no. 4, pp. 113–115, 1991.
  • Li Deng, Non-parametric estimation of phase variance in auditory-nerve fiber’ s responses to tonal stimuli, in Journal of the Acoustical Society of America, vol. 90, no. 6, pp. 3099–3106, 1991.
  • Li Deng, M. Lennig, V. Gupta, and F. Seitz and P. Mermelstein P. Kenny, Phonemic hidden Markov models with continuous mixture output densities for large vocabulary word recognition, in IEEE Transactions on Signal Processing, vol. 39, no. 7, pp. 1677–1681, 1991.
  • Li Deng, The semi-relaxed algorithm for parameter estimation of hidden Markov models, in Computer Speech and Language, vol. 5, no. 3, pp. 231-236, 1991.

1990

  • Li Deng, M. Lennig, and P. Mermelstein, Modeling microsegments of stop consonants in a hidden Markov model based word recognizer, in 2738-2747, vol. 87, pp. 2738-2747, 1990.
  • Li Deng and F. Seitz and P. Mermelstein M. Lennig, Large vocabulary word recognition using context-dependent allophonic hidden Markov models, in Computer Speech and Lan, vol. 4, no. 4, pp. 345-357, 1990.

1989

  • Li Deng, M. Lennig, and P. Mermelstein, Use of vowel duration information in a large vocabulary word recognizer, in Journal of the Acoustical Society of America, vol. 86, pp. 540-548, 1989.

1988

  • Li Deng, D. Geisler, and S. Greenberg, A composite model of the auditory periphery for the processing of speech (invited), in Journal of Phonetics, special theme issue on Representation of Speech in the Auditory Periphery, vol. 16, no. 1, pp. 93-108, 1988.

1987

  • Li Deng and D. C. Geisler, A composite auditory model for processing speech sounds, in Journal of the Acoustical Society of America, vol. 82, no. 6, pp. 2001-2012, 1987.
  • Li Deng and D. Geisler, Responses of auditory-nerve fibers to nasal consonant-vowel syllables, in Journal of the Acoustical Society of America, vol. 82, no. 6, pp. 1977-1988, 1987.
  • Li Deng, D. Geisler, and S. Greenberg, Responses of auditory-nerve fibers to multiple-tone complexes, in Journal of the Acoustical Society of America, vol. 82, no. 6, pp. 1989-2000, 1987.

1986

  • S. Greenberg, D. Geisler, and Li Deng, Frequency selectivity of single cochlear-nerve fibers based on the temporal response pattern of two-tone signals, in Journal of the Acoustical Society of America, vol. 79, no. 4, pp. 1010-1019, 1986.

1985

  • Li Deng and D. Geisler, Changes in the phase of excitor-tone responses in auditory-nerve fibers by suppressor tones, in Journal of the Acoustical Society of America, vol. 78, no. 11, pp. 1633–1644, 1985.
  • D. Geisler and Li Deng, Thresholds for primary auditory fibers using statistically defined criteria, in Journal of the Acoustical Society of America, vol. 77, no. 3, pp. 1102-1109, 1985.

Conference Publications

2016

  • Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf, Deep Reinforcement Learning with a Natural Language Action Space, in Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL), ACL – Association for Computational Linguistics, August 2016.
  • Moontae Lee, Xiaodong He, Wen-tau Yih, Jianfeng Gao, Li Deng, and Paul Smolensky, Reasoning in Vector Space: An Exploratory Study of Question Answering, in Proceedings of the International Conference on Learning Representations (ICLR) 2016, 2 May 2016.
  • Hamid Palangi, Rabab Ward, and Li Deng, EXPLOITING CORRELATIONS AMONG CHANNELS IN DISTRIBUTED COMPRESSIVE SENSING WITH CONVOLUTIONAL DEEP STACKING NETWORKS, IEEE – Institute of Electrical and Electronics Engineers, March 2016.

2015

  • Jianshu Chen, Ji He, Yelong Shen, Lin Xiao, Xiaodong He, Jianfeng Gao, Xinying Song, and Li Deng, End-to-end Learning of Latent Dirichlet Allocation by Mirror-Descent Back Propagation; arXiv:1508.03398; full version to appear in NIPS, Montreal, Canada, December 2015.
  • Hamid Palangi, Rabab Ward, and Li Deng, Distributed Compressive Sensing: A Deep Learning Approach; arXiv:1508.04924 , September 2015.
  • Jacob Devlin, Hao Cheng, Hao Fang, Saurabh Gupta, Li Deng, Xiaodong He, Geoffrey Zweig, and Margaret Mitchell, Language Models for Image Captioning: The Quirks and What Works, ACL – Association for Computational Linguistics, July 2015.
  • Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng, Embedding Entities and Relations for Learning and Inference in Knowledge Bases, in Proceedings of the International Conference on Learning Representations (ICLR) 2015, May 2015.
  • Yelong Shen, Ruoming Jin, Jianshu Chen, Xiaodong He, Jianfeng Gao, and Li Deng, A Deep Embedding Model for Co-occurrence Learning; arXiv:1504.02824, April 2015.
  • Hamid Palangi, Li Deng, Yelong Shen, Jianfeng Gao, Xiaodong He, Jianshu Chen, Xinying Song, and Rabab Ward, Deep Sentence Embedding Using Long Short-Term Memory Networks; arXiv:1502.06922, February 2015.

2014

  • Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng, Learning Multi-Relational Semantics Using Neural-Embedding Models, in NIPS 2014 workshop on Learning Semantics, 12 December 2014.
  • Yelong Shen, Xiaodong He, Jianfeng Gao, Li Deng, and Gregoire Mesnil, A Latent Semantic Model with Convolutional-Pooling Structure for Information Retrieval , CIKM, November 2014.
  • Xiaodong He, Jianfeng Gao, and Li Deng, Deep Learning for Natural Language Processing: Theory and Practice (Tutorial), CIKM, November 2014.
  • Jianfeng Gao, Patrick Pantel, Michael Gamon, Xiaodong He, Li Deng, and Yelong Shen, Modeling Interestingness with Deep Neural Networks, EMNLP, October 2014.
  • Li Deng and John C. Platt, Ensemble Deep Learning for Speech Recognition, Proc. Interspeech, September 2014.
  • Hamid Palangi, Li Deng, and Rabab K Ward, RECURRENT DEEP-STACKING NETWORKS FOR SEQUENCE CLASSIFICATION, IEEE Conference ChinaSIP, July 2014.
  • Jianfeng Gao, Xiaodong He, Wen-tau Yih, and Li Deng, Learning Continuous Phrase Representations for Translation Modeling, in Proceedings of ACL, Association for Computational Linguistics, June 2014.
  • Xiaodong He, Jianfeng Gao, and Li Deng, Deep learning for natural language processing and related applications (Tutorial at ICASSP), IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2014.
  • Li Deng and Jianshu Chen, Sequence classification using the high-level features extracted from deep neural networks, in Proc. ICASSP, May 2014.
  • Jianshu Chen and Li Deng, A Primal-Dual Method for Training Recurrent Neural Networks Constrained by the Echo-State Property, in Proc. International Conf. on Learning Representations (ICLR), April 2014.
  • Yelong Shen, Xiaodong he, Jianfeng Gao, Li Deng, and Gregoire Mesnil, Learning Semantic Representations Using Convolutional Neural Networks for Web Search, WWW 2014, April 2014.

2013

  • Li Deng, Design and Learning of Output Representations for Speech Recognition, in NIPS Workshop, December 2013.
  • Hamid Palangi, Li Deng, and Rabab K Ward, Learning Input and Recurrent Weight Matrices in Echo State Networks, in NIPS Workshop, December 2013.
  • Po-Sen Huang, Xiaodong He, Jianfeng Gao, Li Deng, Alex Acero, and Larry Heck, Learning Deep Structured Semantic Models for Web Search using Clickthrough Data, ACM International Conference on Information and Knowledge Management (CIKM), October 2013.
  • Ossama Abdel-Hamid, Li Deng, and Dong Yu, Exploring Convolutional Neural Network Structures and Optimization Techniques for Speech Recognition, in Interspeech 2013, ISCA, August 2013.
  • Ossama Abdel-Hamid, Li Deng, Dong Yu, and Hui Jiang, Deep segmental neural networks for speech recognition, in Proc. Interspeech, Lyon, France, August 2013.
  • Grégoire Mesnil, Xiaodong He, Li Deng, and Yoshua Bengio, Investigation of Recurrent-Neural-Network Architectures and Learning Methods for Spoken Language Understanding, in Interspeech 2013, August 2013.
  • George Dahl, Jack W. Stokes, Li Deng, and Dong Yu, Large-Scale Malware Classification Using Random Projections and Neural Networks, in Proceedings IEEE Conference on Acoustics, Speech, and Signal Processing, IEEE SPS, 26 May 2013.
  • Jui-Ting Huang, Jinyu Li, Dong Yu, Li Deng, and Yifan Gong, Cross-Language Knowledge Transfer Using Multilingual Deep Neural Network With Shared Hidden Layers, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Li Deng, Ossama Abdel-Hamid, and Dong Yu, A deep convolutional neural network using heterogeneous pooling for trading acoustic invariance with phonetic confusion, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Po-Sen Huang, Kshitiz Kumar, Chaojun Liu, Yifan Gong, and Li Deng, Predicting speech recognition confidence using deep learning with word identity and score features, in Proc. ICASSP, May 2013.
  • Li Deng, Xiaodong He, and Jianfeng Gao, Deep Stacking Networks for Information Retrieval, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Hamid Palangi, Rabab Ward, and Li Deng, Using deep stacking network to improve structured compressive sensing with multiple measurement vectors, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Jennifer Gillenwater, Xiaodong He, Jianfeng Gao, and Li Deng, End-To-End Learning of Parsing Models for Information Retrieval, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Xiaodong He, Li Deng, Dilek Hakkani-Tur, and Gokhan Tur, Multi-Style Adaptive Training for Robust Cross-Lingual Spoken Language Understanding, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Po-Sen Huang, Li Deng, Mark Hasegawa-Johnson, and Xiaodong He, Random Features for Kernel Deep Convex Network, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Li Deng, Geoffrey Hinton, and Brian Kingsbury, New types of deep neural network learning for speech recognition and related applications: An overview, in IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013, May 2013.
  • Li Deng, Jinyu Li, Jui-Ting Huang, Kaisheng Yao, Dong Yu, Frank Seide, Michael Seltzer, Geoff Zweig, Xiaodong He, Jason Williams, Yifan Gong, and Alex Acero, Recent Advances in Deep Learning for Speech Research at Microsoft, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), May 2013.
  • Zhen-Hua Ling, Li Deng, and Dong Yu, Modeling Spectral Envelopes Using Restricted Boltzmann Machines For Statistical Parametric Speech Synthesis, in ICASSP 2013, IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2013.

2012

  • Kaisheng Yao, Dong Yu, Frank Seide, Hang Su, Li Deng, and Yifan Gong, Adaptation Of Context-Dependent Deep Neural Networks For Automatic Speech Recognition, in SLT 2012, December 2012.
  • Li Deng, Gokhan Tur, Xiaodong He, and Dilek Hakkani-Tur, Use of Kernel Deep Convex Networks and End-To-End Learning for Spoken Language Understanding, IEEE Workshop on Spoken Language Technologies, December 2012.
  • Dong Yu, Li Deng, and Frank Seide, Large Vocabulary Speech Recognition Using Deep Tensor Neural Networks, in Interspeech, ISCA, September 2012.
  • Li Deng, Brian Hutchinson, and Dong Yu, Parallel Training of Deep Stacking Networks, in Interspeech, ISCA, September 2012.
  • Gokhan Tur, Li Deng, Dilek Hakkani-Tur, and Xiaodong He, Towards Deeper Understanding Deep Convex Networks for Semantic Utterance Classification, IEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP), March 2012.
  • Xiaodong He and Li Deng, Optimization in Speech-Centric Information Processing: Criteria and techniques, IEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP), March 2012.
  • Amittai Axelrod, Xiaodong He, Li Deng, Alex Acero, and Mei-Yuh Hwang, New Methods and Evaluation Experiments on Translating TED Talks in the IWSLT Benchmark, IEEE International Confrence on Acoustics, Speech, and Signal Processing (ICASSP), March 2012.
  • Dong Yu, Xin Chen, and Li Deng, Factorized Deep Neural Networks for Adaptive Speech Recognition, in IWSML 2012 , March 2012.
  • Li Deng, Dong Yu, and John Platt, Scalable stacking and learning for building deep architectures, in ICASSP 2012, IEEE SPS, March 2012.
  • Dong Yu, Frank Seide, Gang Li, and Li Deng, Exploiting Sparseness In Deep Neural Networks For Large Vocabulary Speech Recognition, in ICASSP 2012, IEEE SPS, March 2012.
  • Brian Hutchinson, Li Deng, and Dong Yu, A deep architecture with bilinear modeling of hidden representations: applications to phonetic recognition, in ICASSP 2012, IEEE SPS, March 2012.
  • Dong Yu, Sabato Siniscalchi, Li Deng, and Chin-Hui Lee, Boosting Attribute And Phone Estimation Accuracies With Deep Neural Networks For Detection-Based Speech Recognition, in ICASSP 2012, IEEE SPS, March 2012.

2011

  • Xiaodong He, Amittai Axelrod, Li Deng, Alex Acero, Mei-Yuh Hwang, Alisa Nguyen, Andrew Wang, and Xiahui Huang, The MSR System For IWSLT 2011 Evaluation, Internaltional Workshop on Spoken Language Translation (IWSLT), December 2011.
  • Li Deng, An Overview of Deep-Structured Learning for Information Processing, in Proc. Asian-Pacific Signal & Information Proc. Annual Summit & Conference (APSIPA-ASC), October 2011.
  • Dong Yu and Li Deng, Accelerated Parallelizable Neural Network Learning Algorithm for Speech Recognition, in Interspeech, International Speech Communication Association, August 2011.
  • Li Deng and Dong Yu, Deep Convex Network: A Scalable Architecture for Speech Pattern Classification, in Interspeech, International Speech Communication Association, August 2011.
  • Xiaodong He and Li Deng, Robust Speech Translation by Domain Adaptation, in Interspeech, International Speech Communication Association, August 2011.
  • Xiaodong He and Li Deng, Discriminative Learning of Feature Functions of Generative Type in Speech Translation, in Workshop on Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing, ICML, July 2011.
  • Jinyu Li, Dong Yu, Li Deng, and Yifan Gong, Towards High-Accuracy Low-Cost Noisy Robust Speech Recognition Exploiting Structured Model , in ICML Workshop 2011, June 2011.
  • Li Deng and Dong Yu, Deep Convex Networks for Image and Speech Classification, in Deep Learning Workshop at 2011 International Conf. Machine Learning , June 2011.
  • Xiaodong He, Li Deng, and Alex Acero, Why Word Error Rate is not a Good Metric for Speech Recognizer Training for the Speech Translation Task?, in Proc. ICASSP, IEEE, May 2011.
  • Yaodong Zhang, Li Deng, Xiaodong He, and Alex Acero, A Novel Decision Function and the Associated Decision-Feedback Learning for Speech Translation, in ICASSP, IEEE, May 2011.
  • G. Dahl, Dong Yu, Li Deng, and Alex Acero, Large Vocabulary Continuous Speech Recognition With Context-Dependent DBN-HMMS, in Proc. ICASSP, Prague, IEEE, May 2011.
  • Li Deng and D. Yu, Deep Convex Network: Architectures and Parallelizable Learning, in The Learning Workshop , April 2011.
  • Jinyu Li, Li Deng, Dong Yu, and Yifan Gong, Towards high-accuracy low-cost noisy robust speech recognition exploiting structured model, in ICML Workshop on Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing, 2011.

2010

  • Henry Li, N. Duan, Yinggong Zhao, Shujie Liu, Lei Cui, M. Hwang, A. Axelrod, Jianfeng Gao, Y. Zhang, and Li Deng, The MSRA Machine Translation System for IWSLT-2010, in Proc. the 7th International Workshop on Spoken Language Translation (IWSLT2010), Paris, France,, December 2010.
  • Dong Yu, Li Deng, and George E. Dahl, Roles of Pre-Training and Fine-Tuning in Context-Dependent DBN-HMMs for Real-World Speech Recognition, in NIPS 2010 workshop on Deep Learning and Unsupervised Feature Learning, December 2010.
  • Chi-Ho Li, Nan Duan, Yinggong Zhao, Shujie Liu, Lei Cui, Mei-yuh Hwang, Amittai Axelrod, Jianfeng Gao, Yaodong Zhang, and Li Deng, DIALOG task: The MSRA Machine Translation System for IWSLT 2010, IWSLT, November 2010.
  • Li Deng, Mike Seltzer, Dong Yu, Alex Acero, Abdel-rahman Mohamed, and Geoff Hinton, Binary Coding of Speech Spectrograms Using a Deep Auto-encoder, in Interspeech 2010, International Speech Communication Association, September 2010.
  • Dong Yu and Li Deng, Deep-Structured Hidden Conditional Random Fields for Phonetic Recognition, in Interspeech 2010, International Speech Communication Association, September 2010.
  • Jinyu Li, Dong Yu, Yifan Gong, and Li Deng, Unscented Transform with Online Distortion Estimation for HMM Adaptation, in Interspeech 2010, International Speech Communication Association, September 2010.
  • Abdel-rahman Mohamed, Dong Yu, and Li Deng, Investigation of Full-Sequence Training of Deep Belief Networks for Speech Recognition, in Interspeech 2010, International Speech Communication Association, September 2010.
  • Dong Yu and Li Deng, Semantic Confidence Calibration for Spoken Dialog Applications, IEEE, March 2010.
  • Dong Yu, Shizhen Wang, Jinyu Li, and Li Deng, Word Confidence Calibration Using a Maximum Entropy Model with Constraints on Confidence and Word Distributions, IEEE, March 2010.
  • Dong Yu, Shizhen Wang, Zahi karam, and Li Deng, Language Recognition Using Deep-Structured Conditional Random Fields, IEEE, March 2010.
  • Dong Yu, Shizhen Wang, Jinyu Li, and Li Deng, Word confidence calibration using a maximum entropy model with constraints on confidence and word distributions, in Proc. ICASSP, 2010.

2009

  • Dong Yu, Li Deng, and Shizhen Wang, Learning in the Deep-Structured Conditional Random Fields, in NIPS 2009 Workshop on Deep Learning for Speech Recognition and Related Applications, December 2009.
  • Dong Yu, Li Deng, and Alex Acero, Hidden Conditional Random Field with Distribution Constraints for Phone Classification, in Interspeech 2009, International Speech Communication Association, September 2009.
  • Oriol Vinyals, Li Deng, Dong Yu, and Alex Acero, Discriminative pronunciation learning using phonetic decoder and minimum classification error criterion, in Proceedings of the ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2009.
  • Dong Yu, Li Deng, Peng Liu, Jian Wu, Yifan Gong, and Alex Acero, Cross-lingual speech recognition under run-time resource constraints, in Proceedings of the ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2009.
  • Balakrishnan Varadarajan, Dong Yu, Li Deng, and Alex Acero, Using collective information in semi-supervised learning for speech recognition, in Proceedings of the ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2009.
  • Balakrishnan Varadarajan, Dong Yu, Li Deng, and Alex Acero, Maximizing global entry reduction for active learning in speech recognition, in Proceedings of the ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2009.
  • Hui Lin, Li Deng, Dong Yu, Yifan Gong, Alex Acero, and Chi-Hui Lee, A Study on Multilingual Acoustic Modeling For Large Vocabulary ASR, in Proceedings of the ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2009.
  • Li Deng, Rethinking of computation for future-generation, knowledge-rich speech recognition and understanding, in IEEE ICME Workshop on Multimedia Signal Processing and Parallel Computing, 2009.

2008

  • Dong Yu, Li Deng, and Alex Acero, The Maximum Entropy Model with Continuous Features , in NIPS Workshop, Whistler, BC, Canada, Microsoft, December 2008.
  • Dong Yu, Li Deng, Jian Wu, Yifan Gong, and Alex Acero, Improvements on Mel-Frequency Cepstrum Minimum-Mean-Square-Error Noise Suppressor for Robust Speech Recognition, in ISCSLP, IEEE, December 2008.
  • Hui Lin, Li Deng, Jasha Droppo, Dong Yu, and Alex Acero, Learning Methods in Multilingual Speech Recognition, in NIPS Workshop, Whistler, BC, Canada, Microsoft, December 2008.
  • Xiaolong Li, Li Deng, Yun-Cheng Ju, and Alex Acero, Automatic Children’s Reading Tutor on Hand-Held Devices, in Proceedings of Interspeech, International Speech Communication Association, Brisbane, Australia, September 2008.
  • Dong Yu, Li Deng, Yifan Gong, and Alex Acero, Parameter Clustering and Sharing in Variable-Parameter HMMs for Noise Robust Speech Recognition, in Proc. of the Interspeech, International Speech Communication Association, September 2008.
  • Dong Yu, Li Deng, Yifan Gong, and Alex Acero, Discriminative Training of Variable-Parameter HMMs for Noise Robust Speech Recognition, in Proceedings of the Interspeech, International Speech Communication Association, September 2008.
  • Tsung-Hui Chang, Zhi-Quan Luo, Li Deng, and Chong-Yung Chi, A Convex Optimization Method for Joint Mean and Variance Parameter Estimation of Large-Margin CDHMM, in Proceedings of the ICASSP, April 2008.
  • Jinyu Li, Li Deng, Dong Yu, Yifan Gong, and Alex Acero, HMM Adaptation Using a Phase-Sensitive Acoustic Distortion Model for Environment-Robust Speech Recognition, Institute of Electrical and Electronics Engineers, Inc., April 2008.
  • Luis Buera, Jasha Droppo, and Alex Acero, Speech Enhancement using a Pitch Predictive Model, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., April 2008.
  • Jinyu Li, Li Deng, Dong Yu, Jian Wu, Yifan Gong, and Alex Acero, Adaptation of compressed HMM parameters for resource-constrained speech recognition, Institute of Electrical and Electronics Engineers, Inc., April 2008.
  • Ivan Tashev, Jasha Droppo, Michael Seltzer, and Alex Acero, Robust Design of Wideband Loudspeaker Arrays, in Proc. of International Conference on Audio, Speech and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., Las Vegas, USA, April 2008.
  • Dong Yu, Li Deng, Jasha Droppo, Jian Wu, Yifan Gong, and Alex Acero, A Minimum Mean-Square-Error Noise Reduction Algorithm on Mel-Frequency Cepstra for Robust Speech Recognition, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., April 2008.
  • Jinyu Li, Li Deng, Dong Yu, Yifan Gong, and Alex Acero, HMM adaptation using a phase-sensitive acoustic distortion model for environment-robust speech recognition, in Proc. ICASSP, 2008.

2007

  • Li Deng, Roles of high-fidelity acoustic modeling in robust speech recognition (invited), in Proceedings IEEE Workshop on Automatic Speech Recognition and Understanding (ASRU), Institute of Electrical and Electronics Engineers, Inc., December 2007.
  • Dong Yu and Li Deng, Large-Margin Discriminative Training of Hidden Markov Models for Speech Recognition (invited), in Proc. IEEE Intern. Conf. Semantic Computing, Irvine, CA, Institute of Electrical and Electronics Engineers, Inc., 17 September 2007.
  • Roberto Togneri and Li Deng, A Structured Speech Model Parameterized by Recursive Dynamics and Neural Networks, in Proc. Interspeech, Antwerp, Belgium, 27 August 2007.
  • Li Deng and H. Strik, Structure-Based and Template-Based Automatic Speech Recognition — Comparing parametric and non-parametric approaches, in Proc. Interspeech, August 2007.
  • Qiang Fu, Xiaodong He, and Li Deng, Phone-Discriminating Minimum Classification Error (P-MCE) Training for Phonetic Recognition, in Proc. Interspeech, August 2007.
  • Dong Yu and Li Deng, Handling Phonetic Context and Speaker Variation in a Structure-Based Speech Recognizer, in Proc. Interspeech, International Speech Communication Association, August 2007.
  • Xiaolong Li, Yun-Cheng Ju, Li Deng, and Alex Acero, Efficient and Robust Language Modeling in an Automatic Children’s Reading Tutor System, in Proceedings of IEEE Internaltional Conference on Acoustics, Speech and Signal Processing (ICASSP), Institute of Electrical and Electronics Engineers, Inc., 18 April 2007.
  • Li Deng and Dong Yu, Use of Differential Cepstra as Acoustic Features in Hidden Trajectory Modeling for Phonetic Recognition, in Proceedings of the ICASSP, Honolulu, Hawaii, IEEE, April 2007.
  • Sibel Yaman, Li Deng, Dong Yu, Ye-Yi Wang, and Alex Acero, A Discriminative Training Framework using N-Best Speech Recognition Transcriptions and Scores for Spoken Utterance Classification, in Proc. of the International Conference on Acoustics, Speech and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., Honolulu, Hawaii, U.S.A., April 2007.
  • Jinyu Li, Li Deng, Dong Yu, Yifan Gong, and Alex Acero, High-Performance HMM Adaptation With Joint Compensation of Additive and Convolutive Distortions Via Vector Taylor Series, in Proceedings IEEE Workshop on ASRU, Institute of Electrical and Electronics Engineers, Inc., April 2007.
  • Dong Yu, Li Deng, Xiaodong, and Alex Acero, Large-Margin Minimum Classification Error Training for Large-Scale Speech Recognition Tasks, in Proceedings of the ICASSP, Honolulu, Hawaii, IEEE, April 2007.
  • Jinyu Li, Li Deng, Dong Yu, Yifan Gong, and Alex Acero, High-performance HMM adaptation with joint compensation of additive and convolutive distortions via vector Taylor series, in Proc. IEEE Automatic Speech Recognition and Understanding, 2007.

2006

  • X. He, Li Deng, and W. Chou, A Novel Learning Method for Hidden Markov Models in Speech and Audio Processing,, in Proc. IEEE Workshop on Multimedia Signal Processing, October 2006.
  • Xiaodong He, Li Deng, and Wu Chou, A novel learning method for hidden Markov models in speech and audio processing, in IEEE MMSP, IEEE SPS, October 2006.
  • Xiaolong Li, Li Deng, Dong Yu, and Alex Acero, A Time-Synchronous Phonetic Decoder For A Long-Contextual-Span Hidden Trajectory Model, in Proceedings of International Conference on Speech Communication (InterSpeech), 2006, International Speech Communication Association, Pittsburgh, PA, 19 September 2006.
  • Dong Yu, Li Deng, Xiaodong He, and Alex Acero, Use of Incrementally Regulated Discriminative Margins in MCE Training for Speech Recognition, in Proc. of the Interspeech Conference, International Speech Communication Association, September 2006.
  • Li Deng, X. Cui, R. Pruvenok, J. Huang, S. Momen, Y. Chen, and A. Alwan, A Database of Vocal Tract Resonance Trajectories for Research in Speech Processing, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2006.

2005

  • Li Deng, Dong Yu, and Alex Acero, A Generative Modeling Framework for Structured Hidden Speech Dynamics, in NIPS Workshop on Advances in Structured Learning for Text and Speech Processing , Microsoft, December 2005.
  • Li Deng, Dong Yu, Xiaolong Li, and Alex Acero, A Long-Contextual-Span Model of Resonance Dynamics for Speech Recognition: Parameter Learning and Recognizer Evaluation, in Proceedings of IEEE Workshop on Automatic Speech Recognition and Understanding, Institute of Electrical and Electronics Engineers, Inc., Puerto Rico, November 2005.
  • Li Deng, Dong Yu, and Alex Acero, Learning Statistically Characterized Resonance Targets in a Hidden Trajectory Model of Speech Coarticulation and Reduction, in Proc. of the Interspeech Conference, International Speech Communication Association, September 2005.
  • Li Deng, Xiaolong Li, Dong Yu, and Alex Acero, Evaluation of a Long-Contextual-Span Hidden Trajectory Model and Phonetic Recognizer Using A* Lattice Search, in Proc. of the Interspeech Conference, International Speech Communication Association, September 2005.
  • A. Subramanya, Li Deng, Z. Liu, and Z. Zhang, Multi-sensory speech processing: Incorporating automatically extracted hidden dynamic information, in Proceedings of the IEEE International Conference on Multimedia & Expo (ICME), Amsterdam, July 2005.
  • Li Deng, Xiang Li, Dong Yu, and Alex Acero, A Hidden Trajectory Model with Bi-Directional Target Filtering: Cascaded vs. Integrated Implementation for Phonetic Recognition, in Proc. of Int. Conf. on Acoustics, Speech, and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., March 2005.

2004

  • Li Deng, Xiaolong Li, Dong Yu, and Alex Acero, Novel Acoustic Modeling with Structured Hidden Dynamics for Speech Coarticulation and Reduction, in Proc. of the DARPA RT04 Workshop, November 2004.
  • Dong Yu, Mei-Yuh Hwang, Peter Mau, Alex Acero, and Li Deng, Unsupervised Learning from Users’ Error Correction in Speech Dictation, in Proc. Int. Conf. on Spoken Language Processing, International Speech Communication Association, October 2004.
  • Li Deng, Dong Yu, and Alex Acero, A Quantitative Model for Formant Dynamics and Contextually Assimilated Reduction in Fluent Speech, in Proc. Int. Conf. on Spoken Language Processing, International Speech Communication Association, October 2004.
  • R. Togneri and Li Deng, Use of Neural Network Mapping and Extended Kalman Filter to Recover Vocal Tract Resonances from the MFCC Parameters of Speech, in Proc. Int. Conf. on Spoken Language Processing, October 2004.
  • Li Deng, Zicheng Liu, Zhengyou Zhang, and Alex Acero, Nonlinear Information Fusion in Multi-Sensor Processing – Extracting and Exploiting Hidden Dynamics of Speech Captured by a Bone-Conductive Microphone, in Proc. of the IEEE Workshop on Multimedia Signal Processing, Institute of Electrical and Electronics Engineers, Inc., September 2004.
  • Li Deng, L. Lee, H. Attias, and Alex Acero, A Structured Speech Model with Continuous Hidden Dynamics and Prediction-Residual Training for Tracking Vocal Tract Resonances, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2004.
  • L. Lee, H. Attias, Li Deng, and P. Fieguth, A Multimodal Variational Approach to Learning and Inference in Switching State Space Models, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2004.
  • Zhengyou Zhang, Z. Liu, M. Sinclair, A. Acero, Li Deng, J. Droppo, Xuedong Huang, and Yanli Zheng, Multisensory microphones for robust speech detection, enhancement, and recognition, in Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Montreal, Canada, May 2004, IEEE, 2004.

2003

  • Y. Zheng, Z. Liu, Z. Zhang, M. Sinclair, Jasha Droppo, Li Deng, Xuedong Huang, and Alex Acero, Air and Bone-Conductive Integrated Microphones for Robust Speech Detection and Enhancement, in Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, Institute of Electrical and Electronics Engineers, Inc., U.S. Virgin Islands, December 2003.
  • J. Wu, Jasha Droppo, Li Deng, and Alex Acero, A Noise-Robust ASR Front-End Using Wiener Filter Constructed from MMSE Estimation of Clean Speech and Noise, in Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, Institute of Electrical and Electronics Engineers, Inc., U.S. Virgin Islands, December 2003.
  • Li Deng, I. Bazzi, and Alex Acero, Tracking Vocal Tract Resonances Using an Analytical Nonlinear Predictor and a Target-guided Temporal Constraint, in Proc. of the Eurospeech Conference. Geneva, September 2003.
  • Jasha Droppo, Li Deng, and Alex Acero, A Comparison of Three Non-Linear Observation Models for Noisy Speech Features, in Proc. Eurospeech Conference, International Speech Communication Association, Geneva, Switzerland, September 2003.
  • Y. Deng, Milind Mahajan, and Alex Acero, Estimating Speech Recognition Error Rate without Acoustic Test Data, in Proc. of the Eurospeech Conference, September 2003.
  • Li Deng, Jasha Droppo, and Alex Acero, Incremental Bayes Learning with Prior Evolution for Tracking Non-Stationary Noise Statistics from Noisy Speech Data, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., Hong Kong, April 2003.
  • H. Attias, L. Lee, and Li Deng, Variational Inference and Learning for Segmental Switching State Space Models of Hidden Speech Dynamics, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, April 2003.
  • Issam Bazzi, Alex Acero, and Li Deng, An Expectation-Maximization Approach for Formant Tracking using a Parameter-free Nonlinear Predictor, in Proc. of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., April 2003.
  • Frank Seide, Jian-Lai Zhou, and Li Deng, Coarticulation Modeling by Embedding a Target-Directed Hidden Trajectory Model into HMM–MAP Decoding and Evaluation, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hong Kong, 2003.
  • Jianlai Zhou, Frank Seide, and Li Deng, Coarticulation Modeling by Embedding a Target-Directed Hidden Trajectory Model into HMM–Model and Training, in Proc. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Hong Kong, 2003.

2002

  • Li Deng, Alex Acero, Ye-Yi Wang, Kuansan Wang, Hsiao-Wuen Hon, Jasha Droppo, Milind Mahajan, and XD Huang, A speech-centric perspective for human-computer interface, in Proc. of the IEEE Fifth Workshop on Multimedia Signal Processing, Institute of Electrical and Electronics Engineers, Inc., December 2002.
  • Li Deng, Jasha Droppo, and Alex Acero, Exploiting Variances in Robust Feature Extraction Based on a Parametric Model of Speech Distortion, in Proc. International Conference on Spoken Language Processing, Denver, Colorado, September 2002.
  • Li Deng, Jasha Droppo, and Alex Acero, Log-Domain Speech Feature Enhancement Using Sequential MAP Noise Estimation and a Phase-sensitive Model of the Acoustic Environment, in Proc. International Conference on Spoken Language Processing, Denver, Colorado, September 2002.
  • Jasha Droppo, Alex Acero, and Li Deng, A Nonlinear Observation Model for Removing Noise from Corrupted Speech Log Mel-Spectral Energies, in Proc. International Conference on Spoken Language Processing, Denver, Colorado, September 2002.
  • Jasha Droppo, Li Deng, and Alex Acero, Evaluation of SPLICE on the Aurora 2 and 3 Tasks, in Proc. International Conference on Spoken Language Processing, International Speech Communication Association, Denver, Colorado, September 2002.
  • Li Deng, Jasha Droppo, and Alex Acero, A Bayesian Approach to Speech Feature Enhancement using the Dynamic Cepstral Prior, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., Florida, May 2002.
  • Jasha Droppo, Li Deng, and Alex Acero, Uncertainty Decoding with SPLICE for Noise Robust Speech Recognition, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., Florida, May 2002.
  • Hagai Attias and Li Deng, A new approach to speech enhancement by a microphone array using EM and mixture moels, in Proceedings of the International Conference on Spoken Language Processing, Denver CO, September 2002, 2002.

2001

  • Li Deng, B. Frey, and T. Kristjansson, Joint Estimation of Noise and Channel Distortion in a Generalized EM Framework, in IEEE Workshop on Automatic Speech Recognition and Understanding, December 2001.
  • Li Deng, Jasha Droppo, and Alex Acero, Recursive Noise Estimation Using Iterative Stochastic Approximation for Stereo-based Robust Speech Recognition, in Proc. IEEE Workshop on Automatic Speech Recognition and Understanding, Institute of Electrical and Electronics Engineers, Inc., Madonna di Campliglio, Italy, December 2001.
  • B. Frey, Li Deng, T. Kristjansson, and Alex Acero, ALGONQUIN: Iterating Laplace’s Method to Remove Multiple Types of Acoustic Distortion for Robust Speech Recognition, in Proc. of the Eurospeech Conference, September 2001.
  • Jasha Droppo, Alex Acero, and Li Deng, Evaluation of the SPLICE Algorithm on the Aurora 2 Database, in Proc. Eurospeech Conference, International Speech Communication Association, Aalbodk, Denmark, September 2001.
  • H. Attias, Li Deng, Alex Acero, and John Platt, A New Method for Speech Denoising and Robust Speech Recognition Using Probabilistic Models for Clean Speech and for Noise, in Proc. of the Eurospeech Conference, September 2001.
  • L. Lee, P. Fleguth, and Li Deng, A Functional Articulatory Dynamic Model for Speech Production, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2001.
  • T. Kristjansson, B. Frey, Li Deng, and Alex Acero, Towards Non-Stationary Model-Based Noise Adaptation for Large Vocabulary Speech Recognition, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2001.
  • R. Togneri and Li Deng, An EKF-Based Algorithm for Learning Statistical Hidden Dynamic Model Parameters for Phonetic Recognition, in Proc. of the Int. Conf. on Acoustics, Speech, and Signal Processing, May 2001.
  • Jasha Droppo, Alex Acero, and Li Deng, Efficient Online Acoustic Environment Estimation for FCDCN in a Continuous Speech Recognition System, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., Salt Lake City, Utah, May 2001.
  • Li Deng, Alex Acero, L. Jiang, Jasha Droppo, and Xuedong Huang, High-Performance Robust Speech Recognition Using Stereo Training Data, in Proc. ICASSP, Institute of Electrical and Electronics Engineers, Inc., Salt Lake City, Utah, May 2001.
  • B. Frey, T. Kristjansson, Li Deng, and Alex Acero, Learning dynamic noise models from noisy speech for robust speech recognition, in Advances in Neural Information Processing Systems (NIPS), Vol. 14, Vancouver, Canada, 2001, pp. 101-108, 2001.
  • Xuedong Huang, Alex Acero, C. Chelba, Li Deng, Jasha Droppo, D. Duchene, J. Goodman, Hsiao-Wuen Hon, D. Jacoby, L. Jiang, R. Loynd, Milind Mahajan, P. Mau, S. Meredith, S. Mughal, S. Neto, M. Plumpe, K. Stery, G. Venolia, Kuansan Wang, and Ye-Yi Wang, MIPAD: A Multimodal Interactive Prototype, in International Conference on Acoustics, Speech, and Signal Processing, Institute of Electrical and Electronics Engineers, Inc., Salt Lake City, Utah, USA, 2001.

2000

  • H. Attias, J. Platt, Alex Acero, and Li Deng, Speech Denoising and Dereverberation Using Probabilistic Models, in NIPS, November 2000.
  • Li Deng, Alex Acero, M. Plumpe, and Xuedong Huang, Large-Vocabulary Speech Recognition under Adverse Acoustic Environments,, in Proc. Int. Conf. on Spoken Language Processing, October 2000.
  • J. Sun, X. Jing, and Li Deng, Data-driven Model Construction for Continuous Speech Recognition Using Overlapping Articulatory Features, in Proc. of the Int. Conf. on Spoken Language Processing, October 2000.
  • Alex Acero, Li Deng, T. Kristjansson, and J. Zhang, HMM Adaptation Using Vector Taylor Series for Noisy Speech Recognition, in Proc. Int. Conf. on Spoken Language Processing, October 2000.
  • H. Jiang and Li Deng, A Robust Training Strategy Against Straneous Acoustic Variations for Spontaneous Speech Recognition, in Proc. of the Int. Conf. on Spoken Language Processing, October 2000.
  • Li Deng, Switching Dynamic System Models for Speech Articulation and Acoustics, in Proc. of the IMA Workshop, September 2000.
  • Xuedong Huang, Alex Acero, Ciprian Chelba, Li Deng, Doug Duchene, Joshua Goodman, Hsiao-Wuen Hon, Derek Jacoby, Li Jiang, Ricky Loynd, Milind Mahajan, Peter Mau, Scott Meredith, Salman Mughal, Salvado Neto, Mike Plumpe, Kuansan Wang, and Ye-Yi Wang, MiPad: A Next Generation PDA Prototype, in International Conference on Spoken Language Processing, International Speech Communication Association, Beijing, China, 2000.

1998

  • M. Naito, Li Deng, and Y. Sagisaka, Speaker clustering for speech recognition using the parameters characterizing vocal tract dimensions, in Proc. ICASSP, March 1998.

1997

  • C. Rathinavelu and Li Deng, Speech adaptation experiments using nonstationary-state HMMs: A MAP approach, in Proc. ICASSP, 1997.
  • Li Deng, Integrated-multilingual speech recognition using universal phonological features in a functional speech production model, in Proc. ICASSP, 1997.

1996

  • C. Rathinavelu and Li Deng, HMM-based speech recognition using state-dependent, discriminatively derived transforms on Mel-warped DFT features, in Proc. ICASSP, 1996.
  • Li Deng and Jim Wu, Hierarchical partitioning of articulatory state space for articulatory-feature based speech recognition, in Proc. ICSLP, 1996.
  • Jim Wu and Li Deng, Acoustic Modeling for Continuous Mandarin-Chinese Speech Recognition, in Proc. ICSLP, 1996.
  • C. Rathinavelu and Li Deng, Trended HMM with discriminative training for phonetic classification, in Proc. ICSLP, 1996.

1995

  • Li Deng, G. Ramsay, and H. Sameti, From modeling surface phenomena to modeling mechanisms: Towards a faithful model of the speech process aiming at speech recognition, in Proceedings of the 1995 IEEE Workshop on Automatic Speech Recognition, 1995.
  • J. Sun and L. Deng. “Annotation and use of speech production corpus for building language-universal speech recognizers”, Proceedings of the 2nd International Symposium on Chinese Spoken Language Processing (ISCSLP), Beijing, October 2000, Vol. 3, pp. 31-34.
  • J. Sun, R. Tongneri and L. Deng. “A robust speech understanding system using conceptual relational grammar,” Proceedings of the International Conference on Spoken Language Processing,October 2000, Vol. 2, pp. 879-882.
  • S. Dusan and L. Deng. “Acoustic-to-articulatory inversion using dynamical and phonological constraints” Proceedings of the 5th Speech Production Workshop: MODELS AND DATA, Kloster Seeon, Germany, May 1-4, 2000, pp. 237-240.
  • M. Naito, L. Deng, and Y. Sagisaka. “Speaker adaptation methods using vocal tract parameters,” (in Japanese) Proceedings of the 1998 Spring Meeting of the Acoustical Society of Japan, Yokohama, Japan, March 17-19, 1998, pp. 55-56.
  • M. Naito, L. Deng, and Y. Sagisaka. “A study on speaker clustering methods using vocal tract parameters,” (in Japanese) Proceedings of Japan Institute of Electronics, Information, and Communication Engineers (IEICE), Yokosuka, Japan, December 1997, Vol. 97, No. 441, pp. 35-40.
  • L. Deng (invited). “A dynamic, feature-based approach to speech modeling and recognition,” Proceedings of the 1997 IEEE Workshop on Automatic Speech Recognition and Understanding, Santa Barbara, CA, December 14-17, 1997, pp. 107-114.
  • X. Shen, L. Deng, and A. Yasmin. “H-infinity filtering for speech enhancement,” Proceedings of the International Conference on Spoken Language Processing, Philadelphia, PA, October 3-6, 1996, pp. 873-876.
  • L. Deng, X. Shen, and D. Jamieson. “Simulation of disordered speech using a frequency-domain vocal tract model,” Proceedings of the International Conference on Spoken Language Processing, Philadelphia, PA, October 3-6, 1996, pp. 768-771.
  • D. Jamieson, L. Deng, M. Price, V. Parsa, and J. Till. “Interactions of speech disorders with speech coders: Effects on speech intelligibility,” Proceedings of the International Conference on Spoken Language Processing, Philadelphia, PA, October 3-6, 1996, pp. 737-740.
  • G. Ramsay and L. Deng. “Optimal filtering and smoothing for speech recognition using a stochastic target model,” Proceedings of the International Conference on Spoken Language Processing, Philadelphia, PA, October 3-6, 1996, pp. 1113-1116.
  • L. Deng, G. Ramsay, and D. Sun. (invited). “Production models as a structural basis for automatic speech recognition,” Proceedings of the Fourth European Speech Production Workshop, Autrans, France, May 24-27, 1996, pp. 69–80.
  • L. Deng. “Finite-state automata derived from overlapping articulatory features: A novel phonological construct for speech recognition,” Proceedings of the Workshop on Computational Phonology in Speech Technology, (published by Association for Computational Linguistics), Santa Cruz, CA, June 28, 1996. pp. 37-45.
  • L. Deng and H. Sheikhzadeh. “Temporal and rate aspects of speech encoding in the auditory system: Simulation results on TIMIT data using a layered neural network interfaced with a cochlear model,” Proceedings of European Speech Communication Association Tutorial and Research Workshop on the Auditory Basis of Speech Recognition, July 15 – 19, 1996, Keele University, United Kingdom, pp. 75-78.
  • C. Rathinavelu and L. Deng. “HMM-based speech recognition using state-dependent, discriminatively derived transforms on Mel-warped DFT features”, Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.1, Atlanta, Georgia, May 7-10, 1996, pp. 9–12.
  • L. Deng, G. Ramsay, and H. Sameti. “From modeling surface phenomena to modeling mechanisms: Towards a faithful model of the speech process aiming at speech recognition,” Proceedings of the 1995 IEEE Workshop on Automatic Speech Recognition, December 10-13, 1995, Snowbird, Utah, pp. 183-184.
  • G. Ramsay and L. Deng. “Maximum-likelihood estimation for articulatory speech recognition using a stochastic target model,” Proceedings of the 1995 European Conference on Speech Communication and Technology, Spain, September 18-21, 1995, pp. 1401-1404.
  • G. Ramsay and L. Deng. “Modal analysis of acoustic wave propagation in the vocal tract using a finite-difference method,” Proceedings of the XII International Congress of Phonetic Sciences, Stockholm, Sweden, August 13-19, 1995, Vol 2, pp. 338-341.
  • G. Ramsay and L. Deng. “Articulatory synthesis using a stochastic target model of speech production,” Proceedings of the XII International Congress of Phonetic Sciences, Stockholm, Sweden, August 13-19, 1995, Vol 2, pp. 478-481.
  • L. Deng, J. Wu, and H. Sameti. “Improved speech modeling and recognition using multi-dimensional articulatory states as primitive speech units,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, May 8-12, 1995, pp. 385-388.
  • D. Sun and L. Deng. “Analysis of acoustic-phonetic variations in fluent speech using TIMIT,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, May 8-12, 1995, pp. 201-204.
  • C. Rathinavelu and L. Deng. “Use of generalized dynamic feature parameters for speech recognition: Maximum likelihood and minimum classification error approaches,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, May 8-12, 1995, pp. 373-376.
  • S. Shen and L. Deng. “Discrete H-infinity filtering design with application to speech enhance ment,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Detroit, MI, May 8-12, 1995, pp. 1504-1507.
  • H. Sheikhzadeh, R. Brennan, L. Deng, and H. Sameti, “Real-time implementation of HMM-based MMSE algorithm for speech enhancement in hearing aid applications,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, 1995 ,
  • D. Sun and L. Deng. “Nonstationary-state hidden Markov model with state-dependent time warping: Application to speech recognition,” Proceedings of the 1994 International Conference on Spoken Language Processing, Vol. 1, Yokohama, Japan, September, 18-22, 1994. pp. 243–246,
  • L. Deng and H. Sameti. “Speech recognition using dynamically defined speech units,” Proceedings of the 1994 International Conference on Spoken Language Processing, Vol. 4, pp. 2167-2170, Yokohama, Japan, September, 18-22, 1994.
  • H. Sheikhzadeh and L. Deng. “Interval statistics from a cochlear model in response to speech sounds,” Journal of the Acoustical Society of America, Vol. 95, No. 6, June 1994 (Abstract), pp. 2842. (The 127th Meeting of the Acoustical Society of America, June 4-8, 1994, Cambridge, MA.)
  • L. Deng and I. Kheirallah. “Stability analysis on finite-difference solution of a basilar-membrane vibration model with application to acoustic signal processing,” Journal of the Acoustical Society of America, Vol. 95, No. 6, June 1994 (Abstract), pp. 2840. (The 127th Meeting of the Acoustical Society of America, June 4-8, 1994, Cambridge, MA.)
  • L. Deng and H. Sameti. “Articulatory phonology and speech recognition: A study on use of dynamically defined speech primitives,” Journal of the Acoustical Society of America, Vol. 95, No. 6, June 1994 (Abstract), pp. 2870. (The 127th Meeting of the Acoustical Society of America, June 4-8, 1994, Cambridge, MA.)
  • G. Ramsay and L. Deng. “A stochastic framework for articulatory speech recognition,” Journal of the Acoustical Society of America, Vol. 95, No. 6, June 1994 (Abstract), pp. 2871. (The 127th Meeting of the Acoustical Society of America, June 4-8, 1994, Cambridge, MA.)
  • K. Hassanein, L. Deng and M. Elmasry. “A neural predictive hidden Markov model for speaker recognition,” Proceedings of the Workshop on Automatic Speaker Recognition, Identification and Verification, Martigny, Switzerland, April, 1994, pp. 115-118.
  • L. Deng and M. Aksmanovic. “HMMs with mixtures of trended functions for automatic speech recognition,” IEEE International Conference on Speech, Image Processing and Neural Networks, April 13-15, 1994, HongKong, pp. 702-705.
  • L. Deng. “A theory on optimal construction of dynamic features for hidden Markov modeling of speech,” IEEE International Conference on Speech, Image Processing and Neural Networks, April 13-15, 1994, HongKong, pp. 351-354.
  • L. Deng and D. Sun. “Phonetic classification and recognition using HMM representation of overlapping articulatory features for all classes of English sounds,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelaide, Australia, April 19-22, 1994, Vol. 1, pp. 45-48.
  • K. Hassanein, L. Deng and M. Elmasry. “Vowel classification using a neural predictive HMM: A discriminative training approach,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelaide, Australia, April 19-22, 1994, Vol 2, pp. 665-668.
  • H. Sameti, H. Sheikhzadeh, L. Deng and R. Brennan. “Comparative performance of spectral subtraction and HMM-based speech enhancement strategies with application to hearing aid design.” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Adelaide, Australia, April 19-22, 1994, Vol. 1, pp. 13-16.
  • L. Deng. “A computational model of phonology-phonetics integration for automatic speech recognition,” Proceedings of the 1993 IEEE Workshop on Automatic Speech Recognition, December 12-15, 1993, Snowbird, Utah, pp. 83–84.
  • K. Hassanein, L. Deng and M. Elmasry. “A neural predictive hidden Markov model for speech and speaker recognition,” Proceedings of the Fifth International Conference on Microelectronics December 14-16, 1993, Dhahran, Saudi Arabia, pp. 108-111.
  • L. Deng and D. Sun. “Speech recognition using the atomic speech units constructed from overlapping articulatory features,” Proceedings of the 1993 European Conference on Speech Communication and Technology, September 21-23, 1993, Berlin, Germany, Vol. III, pp. 1635–1638.
  • D. Zhang, L. Deng, and M. Elmasry. “Pipelined neural network architecture for speech recognition,” Proceedings of the 1993 World Congress on Neural Networks, July 11-15, 1993, Portland, Oregon, Vol. III, pp. 55-58.
  • L. Deng. “Design of a feature-based speech recognizer aiming at integration of auditory processing, signal modeling, and phonological structure of speech.” (invited) Journal of the Acoustical Society of America, Vol. 93, No.4, Pt. 2, pp. 2318, April, 1993.
  • K. Hassanein, L. Deng, and M. Elmasry. “Maximal mutual information training of a neural predictive HMM speech recognition system,” Proceedings of the 1992 IEEE Workshop on Neural Networks for Signal Processing, August 31–September 2, 1992, Copenhagen, Denmark, pp. 164-173.
  • K. Erler and L. Deng. “HMM representation of quantized articulatory features for recognition of highly confusible words,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, San Francisco, CA., March, 1992, pp.545-548.
  • L. Deng. “Speech modeling and recognition using a time series model containing trend functions with Markov modulated parameters,” Proceedings of the 1991 IEEE Workshop on Automatic Speech Recognition, Arden House, New York, December, 1991, pp. 24-26.
  • L. Deng and K. Erler. “Microstructural speech units and their HMM representation for discrete utterance speech recognition,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Toronto, Ontario, Canada, May, 1991, pp. 193–196. P. Seitz, V. Gupta, M. Lennig, P. Kenny, L. Deng, D. O’Shaughnessy, and P. Mermelstein. “Phonological rule set complexity as a factor in the performance of a very large vocabulary word recognition system,” Journal of the Acoustical Society of America, 87(1), May, 1990, S108 (Abstract).
  • L. Deng, V. Gupta, M. Lennig, P. Kenny, and P. Mermelstein. “Acoustic recognition component of an 86,000-word speech recognizer,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Albuquerque, New Mexico, 1990, pp. 741–744.
  • L. Deng, P. Kenny, M. Lennig, V. Gupta and P. Mermelstein. “A locus model of coarticulation in a hidden-Markov-model-based speech recognizer,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, Glascow, Scotland, 1989, pp. 97-100.
  • L. Deng, P. Kenny, M. Lennig, V. Gupta and P. Mermelstein. “Large vocabulary word recognition based on phonetic representation by hidden Markov models”, Proceedings of the Canadian Conference on Electrical and Computer Engineering, Vancouver, Canada, November 1988, pp. 131-134.
  • L. Deng, M. Lennig, and P. Mermelstein. “Modeling acoustic-phonetic detail in a hidden-Markov-model-based large vocabulary speech recognizer,” Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, New York, New York, Vol. 1, April 1988, pp. 509–512.

Patents

Patents (Awarded)

  • Confidence calibration in automatic speech recognition systems, U.S. Patent #9,070,360, granted on June 30, 2015
  • Full-sequence training of deep structures for speech recognition, U.S. Patent #9,031,844, granted on May 12, 2015
  • Deep belief networks for large vocabulary continuous speech recognition, U.S. Patent #8,972,253, granted on March 3, 2015
  • Learning Processes For Single Hidden Layer Neural Networks With Linear Output Units, US Patent #8,918,352, granted on 12/23/2014
  • Exploiting Sparseness in Training Deep Neural Networks, filed 11/28/2011, US Patent #8700552, granted on 4/15/2014
  • Online Distorted Speech Estimation Within An Unscented Transformation Framework. filed on 11/18/2010, US Patent #8731916, granted on 5/20/2014
  • Deep Convex Network With Joint Use Of Nonlinear Random Projection, Restricted Boltzmann Machine And Batch-Based Parallelizable Optimization, filed 3/31/2011, US Patent #8489529, granted on 7/16/2013
  • Deep structured conditional random fields for sequence labeling and classification, U.S. Patent; filed: 1/29/2010; granted on 6/25/2013, Patent #8,473,430
  • Automatic reading feedback with parallel polarized language modeling,” (US Patent #8,433,576, granted on 4/30/2013
  • Generic framework for large-margin MCE training in speech recognition,” (US Patent #8,423,364, granted on 4/16/2013
  • Integrative and discriminative technique for spoken utterance translation (US Patent #8,407,041, granted on 3/26/2013
  • Speech recognition with non-liner noise reduction on Mel-frequency cepstra, (US Patent #8,306,817, granted Nov. 6, 2012)
  • Automatic Reading Tutoring, U.S. Patent; (US Patent #8,306,822, granted Nov. 6, 2012)
  • Adapting A Compressed Model For Use In Speech Recognition,” U.S. Patent, (#8,239,195, granted August 3, 2012)
  • Phase Sensitive Model Adaptation For Noisy Speech Recognition,” U.S. Patent, (#8,214,215, granted July 3, 2012)
  • Minimum classification error training with growth transformation optimization,” (U.S. Patent #8,301,449, granted Oct. 30, 2012)
  • Speech-centric multimodal user interface design in mobile technology,” (US Patent #8,219,406, granted July 10, 2012)
  • High performance HMM adaptation with joint compensation of additive and convolutive distortions,” (US Patent #8,180,637, granted May. 15, 2012)
  • Piecewise-Based Variable-Parameter Hidden Markov Models and the Training Thereof,” (US Patent #8,160,878, granted April 17 2012)
  • Noise Suppressor for Robust Speech Recognition,” (US Patent #8,185,389, granted May. 22, 2012)
  • Parameter Clustering and Sharing for Variable-Parameter Hidden Markov Models, (US Patent #8,145,488, granted March 27, 2012)
  • Parameter Learning in Hidden Trajectory Model, (U.S. Patent #8,010,356, granted August 30, 2011)
  • Time Synchronous Decoding for Long-Span Hidden Trajectory Model, (US patent #7,877,256, granted 2011)
  • Integrated Speech Recognition and Semantic Classification (granted 2011, US patent #7,856,351)
  • Hidden Trajectory Modeling with Differential Cepstra for Speech Recognition, (granted 2010, US patent #7,805,308)
  • Segment-Discriminating Minimum Classification Error Pattern Recognition, with X. He and Q. Fu (granted Jan 18, 2011, US patent #7,873,209)
  • Hidden trajectory modeling with differential cepstra for speech recognition, U.S. Patent No.: 7,805,308; granted on September 28, 2010
  • Time Asynchronous Decoding for Long-Span Trajectory Model,” US patent No.: 7,734,460, granted on June 8, 2010
  • Method and Apparatus for Constructing a Speech Filter Using Estimates of Clean Speech and Noise,” U.S. Patent No.: 7,725,314; granted on May 25, 2010
  • Learning Statistically Characterized Resonance Targets in a Hidden Trajectory Model, US patent #7653535, granted January 2010
  • Incrementally Regulated Discriminative Margins in MCE Training for Speech Recognition, US patent #7617103, granted Sept 2009
  • Quantitative model for formant dynamics and contextually assimilated reduction in fluent speech, US patent No.: 7,565,292, granted on July 21, 2009
  • Acoustic models with structured hidden dynamics with integration over many possible hidden trajectories, US patent No.: 7,565,284, granted on July 21, 2009
  • Speaker-adaptive Learning of Resonance Targets in a Hidden Trajectory Model of Speech Coarticulation, US patent No.: 7,519,531, granted on April 14, 2009
  • Greedy algorithm for identifying values for vocal tract resonance vectors, U.S. Patent No.: 7,475,011; Granted on January 6, 2009
  • Method of Speech Recognition Using Multimodal Variational Inference with Switching State Space Models, U.S. Patent No.: 7,480,615; Granted on January 20, 2009
  • Method of Speech Recognition Using Variables Representing Dynamic Aspects of Speech, U.S. Patent No.: 7,346,510; Granted on March 18, 2008
  • Method of Noise Reduction Using Instantaneous Signal-to-Noise Ratio as the Principal Quantity for Optimal Estimation, U.S. Patent No.: 7,363,221; Granted on April 22, 2008
  • Method and Apparatus for Formant Tracking Using a Residual Model, U.S. Patent No.: 7,424,423; Granted on September 9, 2008
  • Multi-Sensory Speech Enhancement Using Synthesized Sensory Signal, U.S. Patent No.: 7,406,303; Granted on July 29, 2008
  • Two-stage implementation for phonetic recognition using a bi-directional target-directed model of speech co-articulation and reduction, U.S. Patent No.: 7,409,346; Granted on August 5, 2008
  • Removing noise from feature vectors, U.S. Patent No.: 7,310,599; Granted on December 18, 2007
  • Method of determining uncertainty associated with acoustic distortion-based noise reduction, U.S. Patent No. 7,289,955; Granted on October 30, 2007
  • Method and apparatus for identifying noise environments from noisy signals, U.S. Patent No. 7,266,494; Granted on September 4, 2007
  • Method of noisy reduction using correction and scaling vectors with partitioning of the acoustic space in the domain of noisy speech, U.S. Patent No.7,254,536; Granted on August 7, 2007
  • Method of determining uncertainty in noise reduction, US and International Patents; U.S. Patent No.: 7,174,292; Granted on Feb. 6, 2007
  • Method of Noise Estimation Using Incremental Bayes Learning, US. Patent; Patent No.: 7,165,026; Granted on Jan. 16, 2007
  • Method of iterative noise estimation in a recursive framework, U.S. Patent; Patent No. 7,139,703; Granted on Nov. 21, 2006
  • Method of noise reduction using correction vectors based on dynamic aspects of speech and noise normalization, United States Patent No. 7,117,148; Granted on October 3, 2006.
  • Method of noise reduction based on dynamic aspects of speech, United States Patent No. 7,107,210; Granted on Sept 12, 2006
  • Method of pattern recognition using noise reduction uncertainty, United States Patent No. 7,103,540; Granted on Sept 5, 2006
  • Microphone array signal enhancement using mixture models (jointly with Hagai Attias), United States Patent No. 7,103,541; Granted on Sept 5, 2006
  • Efficient backward recursion for computing posterior probabilities, United States Patent No. 7,062,407; Granted on June 13, 2006
  • Method of speech recognition using time-dependent interpolation and hidden dynamics, United States (and International) Patent No. 7,050,975; Granted on May 23, 2006
  • Nonlinear observation models for removing noise from corrupted speech, United States (and International) Patent No. 7,047,047; Granted on May 16, 2006
  • Method of Noise Reduction Using Correction and Scaling Vectors with Partitioning of the Acoustic Space in the Domain of Noisy Speech, United States Patent No. 7,003,455; Granted on February 21, 2006
  • Methods and Apparatus for Denoising and Dereverberation Using Variational Inference and Strong Speech Models, United States Patent No. 6,990,447; Granted on January 24, 2006
  • Method and Apparatus for Removing Noise from Feature Vectors, United States Patent No. 6,985,858; Granted on January 10, 2006
  • Methods for Including the Category of Environmental Noise When Processing Speech Signals, United States Patent No. 6,959,276; Granted on October 25, 2005
  • Method of iterative noise estimation in a recursive framework, United States Patent; Patent No. 6,944,590; Granted on September 13, 2005
  • Method of speech recognition using variational inference with switching state space models, United States Patent; Patent No. 6,931,374; Granted on August 16, 2005
  • Pattern Recognition Training Method and Apparatus Using Inserted Noise Followed by Noise Reduction, United States (and International) Patent; Patent No. 6,876,966; Granted on April 5, 2005
  • Apparatus for Speaker Clustering and for Speech Recognition, Patent No.: 2,965,537; Granted on Aug. 13, 1999; Countries of issue: United States and Japan
  • Apparatus for Speaker Normalization Processor and for Voice Recognition Device, Patent No.: 2986792; Granted on Oct. 1, 1999; Countries of issue: United States and Japan

Patents (Pending Awards)

  • Method of speech recognition using hidden trajectory hidden Markov models, U.S. Patent
  • Zero-variance model of acoustic environment for enhancing noisy speech features,” U.S. Patent
  • Method and Apparatus for Multi-Sensory Speech Enhancement,” International Patent;Method and apparatus for continuous valued vocal tract resonance tracking using piecewise linear approximation
  • Speech resonance target estimation using formant tracking results, U.S. Patent
  • Incrementally regulating discriminative margins in MCE training for speech recognition,” U.S. Patent; filing date: 8/25/2006
  • Using a discretized, higher order representation of hidden dynamic variables for speech recognition,” U.S. Patent; filing date: 8/21/2006
  • Integrated speech recognition and semantic classification,” U.S. Patent; filing date: 1/19/2007
  • Segment-discriminating minimum classification error pattern recognition,” U.S. Patent; filing date: 1/31/2007
  • Maximum Entropy Model with Continuous Features, U.S. Patent; filing date: April 2009Cross-lingual speech recognition with HMM using KL distance,” U.S. Patent; filing date: April 2009
  • Maximum entropy model with continuous features, U.S. Patent; filing date: 4/1/2009
  • Discriminative learning of feature functions of generative type in speech translation, filed 10/28/2011
  • Discriminative pretraining of deep neural networks, filed 11/26/2011
  • Tensor Deep Stacking Networks, filed 2/15/2012
  • Computer-Implemented Deep Tensor Neural Network, filed 8/29/2012
  • Multilingual Deep Neural Network, filed 3/11/2013
  • Assignment of semantic labels to a sequence of words using neural network architectures, filed 9/2/2013
  • Deep structured semantic model produced using click-through data. filed 9/6/2013
  • Convolutional Latent Semantic Models and Their Applications. filed 4/1/2014
  • Context-Sensitive Search Using a Deep Learning Model, filed 4/14/2014
  • Modeling Interestingness with Deep Neural Networks, filed 6/13/2014
  • Training and operations of computational models, US patent filed 6/29/2015

Tech Reports and Special Reprints

  • Li Deng and Navdeep Jaitly, Deep Discriminative and Generative Models for Pattern Recognition, no. MSR-TR-2015-59, November 2015.
  • Li Deng, Deep generative and discriminative models for speech recognition, no. MSR-TR-2015-76, October 2015.
  • Li Deng, Deep Learning for Speech and Language Processing — From machine learning and signal processing perspectives; Tutorial at Interspeech 2015, no. MSR-TR-2015-77, September 2015.
  • Li Deng, Fundamentals of Speech Recognition, no. MSR-TR-2015-60, July 2015.
  • Li Deng, Achievements and Challenges of Deep Learning, no. MSR-TR-2015-42, May 2015.
  • Jianfeng Gao, Xiaodong He, and Li Deng, Deep Learning for Web Search and Natural Language Processing, no. MSR-TR-2015-7, January 2015.
  • D. Yu and L. Deng, Signal and Communication Technology: ASR — Deep Learning, no. MSR-TR-2014-156, December 2014.
  • Xinying Song, Xiaodong He, Jianfeng Gao, and Li Deng, Unsupervised Learning of Word Semantic Embedding using the Deep Structured Semantic Model, no. MSR-TR-2014-109, August 2014.
  • Wei-Yun Ma, Yun-Cheng Ju, Xiaodong He, and Li Deng, Language Model Adaptation through Shared Linear Transformations, no. MSR-TR-2014-90, June 2014.
  • Li Deng and Dong Yu, Deep Learning: Methods and Applications, no. MSR-TR-2014-21, May 2014.
  • Jianfeng Gao, Xiaodong He, Wen-tau Yih, and Li Deng, Learning Semantic Representations for the Phrase Translation Model, no. MSR-TR-2013-88, September 2013.
  • Li Deng and Linda Cherry and IEEE SPS Staff, Reprints of Selcted Articles with Brazilian Portuguese Translation, IEEE SPS, October 2011.
  • Li Deng and Linda Cherry and IEEE SPS Staff, Reprints of Selcted Articles with Chinese Translation, , IEEE SPS, September 2011.
  • Li Deng and IEEE SPS Staff, Reprints of Selcted Articles with Chinese Translation, IEEE SPS, September 2010.
  • J. Baker, Li Deng, S. Khudanpur, C.-H. Lee, James Glass, and N. Morgan, Historical Development and Future Directions in Speech Recognition and Understanding, no. MSR-TR-2007-145, October 2007.
  • Xiaodong He and Li Deng, Discriminative Learning in Speech Recognition, no. MSR-TR-2007-129, October 2007.

Dissertations

News