Foundations and Trends(r) in Signal Processing

Deep Learning: Methods and Applications provides an overview of general deep learning methodology and its applications to a variety of signal and information processing tasks. The application areas are chosen with the following three criteria in mind: (1) expertise or knowledge of the authors; (2) the application areas that have already been transformed by the successful use of deep learning technology, such as speech recognition and computer vision; and (3) the application areas that have the potential to be impacted significantly by deep learning and that have been benefitting from recent research efforts, including natural language and text processing, information retrieval, and multimodal information processing empowered by multi-task deep learning. Deep Learning: Methods and Applications is a timely and important book for researchers and students with an interest in deep learning methodology and its applications in signal and information processing. "This book provides an overview of a sweeping range of up-to-date deep learning methodologies and their application to a variety of signal and information processing tasks, including not only automatic speech recognition (ASR), but also computer vision, language modeling, text processing, multimodal learning, and information retrieval. This is the first and the most valuable book for "deep and wide learning" of deep learning, not to be missed by anyone who wants to know the breathtaking impact of deep learning on many facets of information processing, especially ASR, all of vital importance to our modern technological society." - Sadaoki Furui, President of Toyota Technological Institute at Chicago, and Professor at the Tokyo Institute of Technology.

Li Deng

Li Deng (IEEE M'89;SM'92;F'04) received the Ph.D. degree from the University of Wisconsin-Madison. 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, WA, where he is currently Partner Research Manager of the Deep Learning Technology Center. Since 2000, he has also been an Affiliate Full Professor and graduate committee member at the University of Washington, Seattle, teaching a graduate course of Computer Speech Processing and serving on Ph.D. thesis committees. Prior to joining Microsoft, he conducted research and taught at Massachusetts Institute of Technology, ATR Interpreting Telecom. Research Lab. (Kyoto, Japan), and HKUST. He has been granted over 70 US or international patents in acoustics/audio, speech/language technology, large-scale data analysis, and machine learning with recent focus on deep learning. He received numerous awards/honors bestowed by IEEE, ISCA, ASA, 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.

Dong Yu

Dr. Dong Yu (俞栋) joined Microsoft Corporation in 1998 and the Microsoft Speech and Dialog Research Group in 2002, where he currently is a principal researcher. He holds a Ph.D. degree in computer science from University of Idaho, an MS degree in computer science from Indiana University at Bloomington, an MS degree in electrical engineering from Chinese Academy of Sciences, and a BS degree (with honor) in electrical engineering from Zhejiang University (China). His current research interests include speech processing, robust speech recognition, discriminative training, and machine learning. He has published two books and over 140 papers in these areas and is the inventor/coinventor of near 60 granted/pending patents.

His most recent work focuses on deep learning and its application in large vocabulary speech recognition. The context-dependent deep neural network hidden Markov model (CD-DNN-HMM) he co-proposed and developed has been seriously challenging the dominant position of the conventional GMM based system for large vocabulary speech recognition and helped popularize deep learning. His work was recognized by the IEEE SPS 2013 best paper award.

Dr. Dong Yu is a senior member of IEEE, a member of ACM, and a member of ISCA. He is currently serving as a member of the IEEE Speech and Language Processing Technical Committee (2013-) and an associate editor of IEEE transactions on audio, speech, and language processing (2011-). He has served as an associate editor of IEEE signal processing magazine (2008-2011) and the lead guest editor of IEEE transactions on audio, speech, and language processing - special issue on deep learning for speech and language processing (2010-2011).