학술논문
A Summary of the ComParE COVID-19 Challenges
Document Type
Working Paper
Author
Coppock, Harry; Akman, Alican; Bergler, Christian; Gerczuk, Maurice; Brown, Chloë; Chauhan, Jagmohan; Grammenos, Andreas; Hasthanasombat, Apinan; Spathis, Dimitris; Xia, Tong; Cicuta, Pietro; Han, Jing; Amiriparian, Shahin; Baird, Alice; Stappen, Lukas; Ottl, Sandra; Tzirakis, Panagiotis; Batliner, Anton; Mascolo, Cecilia; Schuller, Björn W.
Source
Subject
Language
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).
Comment: 18 pages, 13 figures
Comment: 18 pages, 13 figures