A Summary of the ComParE COVID-19 Challenges
Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk,, Chlo\"e 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
TL;DR
This paper summarizes the results of the INTERSPEECH 2021 Challenges on detecting COVID-19 from respiratory sounds, highlighting machine learning approaches in digital mass testing.
Contribution
It provides a comprehensive overview of the methods and outcomes from the COVID-19 speech and cough detection challenges in 2021.
Findings
Various machine learning models achieved different levels of accuracy.
The challenges fostered progress in audio-based COVID-19 detection methods.
The results highlight potential and limitations of audio analysis for health diagnostics.
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).
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