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A summary of the ComParE COVID-19 challenges.

Published version
Peer-reviewed

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Authors

Coppock, Harry 
Akman, Alican 
Bergler, Christian 
Gerczuk, Maurice 
Brown, Chloë 

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).

Description

Peer reviewed: True

Keywords

COVID-19, Digital Health, computer audition, deep learning, machine learning

Journal Title

Front Digit Health

Conference Name

Journal ISSN

2673-253X
2673-253X

Volume Title

Publisher

Frontiers Media SA
Sponsorship
European Commission Horizon 2020 (H2020) ERC (833296)