A summary of the ComParE COVID-19 challenges

  • 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).

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Metadaten
Author:Harry Coppock, Alican Akman, Christian Bergler, Maurice GerczukORCiD, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta, Jing Han, Shahin AmiriparianORCiDGND, Alice BairdGND, Lukas Stappen, Sandra Ottl, Panagiotis Tzirakis, Anton BatlinerGND, Cecilia Mascolo, Björn W. SchullerORCiDGND
URN:urn:nbn:de:bvb:384-opus4-992823
Frontdoor URLhttps://opus.bibliothek.uni-augsburg.de/opus4/99282
Parent Title (English):arXiv
Type:Preprint
Language:English
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2022/11/15
First Page:arXiv:2202.08981
DOI:https://doi.org/10.48550/arXiv.2202.08981
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Embedded Intelligence for Health Care and Wellbeing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY 4.0: Creative Commons: Namensnennung (mit Print on Demand)