University of Cambridge > Talks.cam > Signal Processing and Communications Lab Seminars > Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages

Deep Learning for Multifarious Speech Processing: Tackling Multiple Speakers, Microphones, and Languages

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If you have a question about this talk, please contact Dr Ramji Venkataramanan.

Speech processing has been at the forefront of the recent deep learning revolution, with major breakthroughs in automatic speech recognition, speech enhancement, and source separation. I will give an overview of deep learning techniques developed at MERL towards the goal of cracking the Tower of Babel version of the cocktail party problem, that is, separating and/or recognizing the speech of multiple unknown speakers speaking simultaneously in multiple languages. I will also attempt to present live demonstrations with audience participation (weather, time, and network conditions permitting).

Bio: Jonathan Le Roux is a Senior Principal Research Scientist and the Speech and Audio Team Leader at Mitsubishi Electric Research Laboratories (MERL) in Cambridge, Massachusetts. He completed his B.Sc. and M.Sc. degrees in Mathematics at the Ecole Normale Supérieure (Paris, France), his Ph.D. degree at the University of Tokyo (Japan) and the Université Pierre et Marie Curie (Paris, France), and worked as a postdoctoral researcher at NTT ’s Communication Science Laboratories from 2009 to 2011. His research interests are in signal processing and machine learning applied to speech and audio. He has contributed to more than 80 peer-reviewed papers and 20 patents in these fields. He is a founder and chair of the Speech and Audio in the Northeast (SANE) series of workshops, a Senior Member of the IEEE , and a member of the IEEE Audio and Acoustic Signal Processing Technical Committee (AASP).

This talk is part of the Signal Processing and Communications Lab Seminars series.

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