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He also represented the new chair of computer science with a focus on sensor technology at the University of Passau. He received calls to Great Britain in Edinburgh and London — and accepted a position as senior lecturer at Imperial College London , which promoted him in to reader in machine learning , and in to professor of artificial intelligence.
He further accepted a call to the University of Passau as full professor of the newly established chair of complex and intelligent systems initially chair of computer science with a focus on complex systems engineering in In , he accepted a professorship for embedded intelligence for healthcare and wellbeing both in the Faculty of Applied Computer Science and the Faculty of Medicine at the University of Augsburg.
His research interests include machine intelligence , signal processing with a focus on audio, affective computing  and health informatics.
Schuller is author and co-author of more than papers in peer-reviewed books, journals, and conference proceedings. Google Scholar names for these more than 20, citations .saccojuncfast.cf
From Wikipedia, the free encyclopedia. Archived from the original on This book presents the methods, tools and techniques that are currently being used to recognise automatically the affect, emotion, personality and everything else beyond linguistics paralinguistics expressed by or embedded in human speech and language.
- Bjorn Schuller (Author of Computational Paralinguistics).
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It is the first book to provide such a systematic survey of paralinguistics in speech and language processing. The technology described has evolved mainly from automatic speech and speaker recognition and processing, but also takes into account recent developments within speech signal processing, machine intelligence and data mining. Moreover, the book offers a hands-on approach by integrating actual data sets, software, and open-source utilities which will make the book invaluable as a teaching tool and similarly useful for those professionals already in the field.
Explains the history and state of the art of all of the sub-fields which contribute to the topic of computational paralinguistics.
C overs the signal processing and machine learning aspects of the actual computational modelling of emotion and personality and explains the detection process from corpus collection to feature extraction and from model testing to system integration. Details aspects of real-world system integration including distribution, weakly supervised learning and confidence measures.
Outlines machine learning approaches including static, dynamic and context sensitive algorithms for classification and regression. Includes a tutorial on freely available toolkits, such as the open-source openEAR toolkit for emotion and affect recognition co-developed by one of the authors, and a listing of standard databases and feature sets used in the field to allow for immediate experimentation enabling the reader to build an emotion detection model on an existing corpus.