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arXiv 2202.08981cs.SDcs.LGeess.AS
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ComParE COVID-19挑战总结

A Summary of the ComParE COVID-19 Challenges

Imperial College London（帝国理工学院）；FAU Erlangen-Nürnberg（埃尔朗根-纽伦堡大学）；Universität Augsburg（奥格斯堡大学）；University of Cambridge（剑桥大学）；University of Southampton（南安普顿大学）
Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong… 展开作者

Harry Coppock, Alican Akman, Christian Bergler, Maurice Gerczuk, Chloë 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, Anton Batliner, Cecilia Mascolo, Björn W. Schuller

备注：
18 pages, 13 figures
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摘要：

COVID-19大流行造成了巨大的人道主义和经济损失。来自广泛学科的科学家团队一直在寻找帮助政府和社区对抗这种疾病的方法。已经探索的机器学习领域的一条途径是数字质量测试的前景，该测试可以从感染者的呼吸声中检测COVID-19。我们总结了INTERSPEECH 2021计算副语言学挑战赛的结果：COVID-19咳嗽（CCS）和COVID-19语音（CSS）。

英文摘要：

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).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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