Frontiers in Digital Health (Mar 2023)
A summary of the ComParE COVID-19 challenges
- Harry Coppock,
- Alican Akman,
- Christian Bergler,
- Maurice Gerczuk,
- Chloë Brown,
- Jagmohan Chauhan,
- Andreas Grammenos,
- Apinan Hasthanasombat,
- Dimitris Spathis,
- Tong Xia,
- Pietro Cicuta,
- Jing Han,
- Shahin Amiriparian,
- Alice Baird,
- Lukas Stappen,
- Sandra Ottl,
- Panagiotis Tzirakis,
- Anton Batliner,
- Cecilia Mascolo,
- Björn W. Schuller,
- Björn W. Schuller
Affiliations
- Harry Coppock
- Department of Computing, Imperial College London, London, United Kingdom
- Alican Akman
- Department of Computing, Imperial College London, London, United Kingdom
- Christian Bergler
- Department of Computing, FAU Erlangen-Nürnberg, Erlangen-Nürnberg, Germany
- Maurice Gerczuk
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Chloë Brown
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Jagmohan Chauhan
- Department of Computing, University of Southampton, Southampton, United Kingdom
- Andreas Grammenos
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Apinan Hasthanasombat
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Dimitris Spathis
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Tong Xia
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Pietro Cicuta
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Jing Han
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Shahin Amiriparian
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Alice Baird
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Lukas Stappen
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Sandra Ottl
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Panagiotis Tzirakis
- Department of Computing, Imperial College London, London, United Kingdom
- Anton Batliner
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- Cecilia Mascolo
- Department of Computer Science and Technology, University of Cambridge, Cambridge, United Kingdom
- Björn W. Schuller
- Department of Computing, Imperial College London, London, United Kingdom
- Björn W. Schuller
- Institute of Computer Science, Universität Augsburg, Augsburg, Germany
- DOI
- https://doi.org/10.3389/fdgth.2023.1058163
- Journal volume & issue
-
Vol. 5
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).
Keywords
