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Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen
Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen
Foto: Universität Paderborn, Jörg Ullmann Bildinformationen anzeigen

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

Foto: Universität Paderborn, Jörg Ullmann

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Sprachverarbeitungslegende Prof. Chin-Hui Lee spricht über maschinelles Lernen – Mo. 23.7.2018

Prof. Chin-Hui Lee, einer der bekanntesten Persönlichkeiten auf dem Gebiet der automatischen Spracherkennung, besucht am 23.7. die Universität Paderborn. Auf Einladung von Prof. Häb-Umbach, Fachgebiet Nachrichtentechnik, wird er um 14.00h im Raum P1.3.01 einen Vortrag mit dem Thema A Machine Learning Approach to Acoustic Signal Processing halten und steht den ganzen Tag für Fragen und Diskussionen zur Verfügung.

Chin-Hui Lee ist Professor am Georgia Institute of Technology. Davor war er bis zum Jahr 2001 an den berühmten Bell Laboratories, Murray Hill, New Jersey, beschäftigt, wo er als „Distinguished Member of Technical Staff“ zuletzt Direktor der Forschungsabteilung „Dialogue Systems“ war. Dr. Lee ist Fellow des IEEE und der ISCA (International Speech Communication Association). Unter seinen vielen Auszeichnungen ist der renommierte Technical Achievement Award der IEEE Signal Processing Society für “Exceptional Contributions to the Field of Automatic Speech Recognition''.

Vortragsabstract

We cast classical signal pre-processing problems into a new regression setting by learning the nonlinear mapping from noisy speech spectra to clean speech features based on deep neural networks (DNNs), combining the emerging deep learning and big data paradigms. DNN-enhanced speech demonstrates good quality and intelligibility in challenging acoustic conditions. Furthermore, this paradigm facilitates an integrated learning framework to train the three key modules in an automatic speech recognition (ASR) system, namely signal conditioning, feature extraction and acoustic modeling, all altogether in a unified manner. The proposed approach was tested on recent challenging ASR tasks in CHiME-2, CHiME-4 and REVERB, designed to evaluate ASR robustness in mixed speakers, multi-channel, and reverberant conditions, respectively. Leveraging on the top speech qualities achieved in speech separation, microphone array based speech enhancement and speech dereverberation, needed for the three corresponding speaking environments, our team scored the lowest word error rates in all three scenarios.

Kurzbiographie

Professor Chin-Hui Lee, School of ECE, Georgia Tech, USA will give a talk on “A Machine Learning Approach to Acoustic Signal Processing” on Monday, 23 July 2018, at 14:00 in Room P1.3.01 (Faculty meeting room).

Chin-Hui Lee is a professor at School of Electrical and Computer Engineering, Georgia Institute of Technology. Before joining academia in 2001, he had accumulated 20 years of industrial experience ending in Bell Laboratories, Murray Hill, as a Distinguished Member of Technical Staff and Director of the Dialogue Systems Research Department. Dr. Lee is a Fellow of the IEEE and a Fellow of ISCA. He has published over 500 papers and 30 patents, with more than 34,000 citations and an h-index of 75 on Google Scholar. He received numerous awards, including the Bell Labs President's Gold Award in 1998. He won the SPS's 2006 Technical Achievement Award for “Exceptional Contributions to the Field of Automatic Speech Recognition''. In 2012 he gave an ICASSP plenary talk on the future of automatic speech recognition. In the same year he was awarded the ISCA Medal in scientific achievement for “pioneering and seminal contributions to the principles and practice of automatic speech and speaker recognition''.

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