Today, signal processing research has a significantly widened scope compared with just a few years ago, and machine learning has been an important technical area of the IEEE signal processing society. Since 2006, deep learning-a new area of machine learning research-has emerged, impacting a wide range of signal and information processing work within the traditional and the new, widened scopes. Various workshops, such as the 2011 ICML Workshop on Learning Architectures, Representations, and Optimization for Speech and Visual Information Processing, the 2009 ICML Workshop on Learning Feature Hierarchies, the 2008 NIPS Deep Learning Workshop, the 2009 NIPS Workshop on Deep Learning for Speech Recognition and Related Applications, as well as an upcoming special issue on Deep Learning for Speech and Language Processing in IEEE Transactions on Audio, Speech, and Language Processing (2011) have been devoted exclusively to deep learning and its applications to various classical signal processing areas. We have also seen the government sponsor research on deep learning (e.g., the DARPA deep learning program).
The purpose of this tutorial is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work
conducted in this area since the birth of deep learning in 2006 that is of direct relevance to signal processing. Future research directions will be
discussed that may attract interests of and require efforts from more signal processing researchers, students, and practitioners in this emerging area for advancing signal and information processing technology and applications.
Li Deng received the Ph.D. degree from the University of Wisconsin-Madison. He joined Dept. Electrical and Computer Engineering, University of Waterloo, Ontario, Canada in 1989 as an Assistant Professor, where he became a Full Professor with tenure in 1996. From 1992 to 1993, he conducted sabbatical research at Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, Mass, and from 1997-1998, at ATR Interpreting Telecommunications Research Laboratories, Kyoto, Japan. In 1999, he joined Microsoft Research, Redmond, WA as a Senior Researcher, where he is currently a Principal Researcher. Since 2000, he has also been an Affiliate Professor in the Department of Electrical Engineering at University of Washington, Seattle, teaching the graduate course of Computer Speech Processing. His current (and past) research activities include automatic speech and speaker recognition, spoken language identification and understanding, speech-to-speech translation, machine translation, language modeling, statistical methods and machine learning, neural information processing, deep-structured learning, machine intelligence, audio and acoustic signal processing, statistical signal processing and 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 interaction. In these areas, he has published over 300 refereed papers in leading journals and conferences, 3 books, 15 book chapters, and has given keynotes, tutorials, and lectures worldwide. He is elected by ISCA (International Speech Communication Association) as its Distinguished Lecturer 2010-2011. He has been granted over 40 US or international patents in acoustics/audio, speech/language technology, and other fields of signal processing. He received awards/honors bestowed by IEEE, ISCA, ASA, Microsoft, and other organizations.
He is a Fellow of the Acoustical Society of America, and a Fellow of the IEEE. He serves 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 ranks consistently among the top journals with the highest citation impact. According to the Thomson Reuters Journal Citation Report, released June 2010, the SPM has ranked first among all IEEE publications (125 in total) and among all publications within the Electrical and Electronics Engineering Category (245 in total) in terms of its impact factor.