A Summary of the ComParE COVID-19 Challenges
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Abstract (arXiv)
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).
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 23 of its 51 references that are in the map. First posted on arXiv 17 Feb 2022.
Builds on
23 of 51 references.
References · 23
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- 2021DiCOVA Challenge: Dataset, task, and baseline system for COVID-19 diagnosis using acousticscited by 21
11 more
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Citing papers per year: 2023: 1; 2024: 1.
2 papers.