A summary of the ComParE COVID-19 challenges
Coppock H, Akman A, Bergler C, Gerczuk M, Brown C, Chauhan J, Grammenos A, Hasthanasombat A, Spathis D, Xia T, Cicuta P, Han J, Amiriparian S, Baird A, Stappen L, Ottl S, Tzirakis P, Batliner A, Mascolo C, Schuller BW (2023)
Publication Type: Journal article
Publication year: 2023
Journal
Book Volume: 5
Article Number: 1058163
DOI: 10.3389/fdgth.2023.1058163
Abstract
The COVID-19 pandemic has caused massive humanitarian and economic damage. Teams of scientists from a broad range of disciplines have searched for methods to help governments and communities combat the disease. One avenue from the machine learning field which has been explored is the prospect of a digital mass test which can detect COVID-19 from infected individuals’ respiratory sounds. We present a summary of the results from the INTERSPEECH 2021 Computational Paralinguistics Challenges: COVID-19 Cough, (CCS) and COVID-19 Speech, (CSS).
Authors with CRIS profile
Involved external institutions
Imperial College London / The Imperial College of Science, Technology and Medicine
United Kingdom (GB)
Universität Augsburg
Germany (DE)
University of Cambridge
United Kingdom (GB)
University of Southampton
United Kingdom (GB)
United Kingdom (GB)
Universität Augsburg
Germany (DE)
University of Cambridge
United Kingdom (GB)
University of Southampton
United Kingdom (GB)
How to cite
APA:
Coppock, H., Akman, A., Bergler, C., Gerczuk, M., Brown, C., Chauhan, J.,... Schuller, B.W. (2023). A summary of the ComParE COVID-19 challenges. Frontiers in Digital Health, 5. https://dx.doi.org/10.3389/fdgth.2023.1058163
MLA:
Coppock, Harry, et al. "A summary of the ComParE COVID-19 challenges." Frontiers in Digital Health 5 (2023).
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