A Summary of the ComParE COVID-19 Challenges
Sound
2022-02-21 v1 Machine Learning
Audio and Speech Processing
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).
Cite
@article{arxiv.2202.08981,
title = {A Summary of the ComParE COVID-19 Challenges},
author = {Harry Coppock and Alican Akman and Christian Bergler and Maurice Gerczuk and Chloë Brown and Jagmohan Chauhan and Andreas Grammenos and Apinan Hasthanasombat and Dimitris Spathis and Tong Xia and Pietro Cicuta and Jing Han and Shahin Amiriparian and Alice Baird and Lukas Stappen and Sandra Ottl and Panagiotis Tzirakis and Anton Batliner and Cecilia Mascolo and Björn W. Schuller},
journal= {arXiv preprint arXiv:2202.08981},
year = {2022}
}
Comments
18 pages, 13 figures