Thu-3-9-1 An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety

Jing Han(University of Augsburg), Kun Qian(The University of Tokyo), Meishu Song(Univeristy of Augsburg), Zijiang Yang(ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany), Zhao Ren(University of Augsburg), Shuo Liu(ZD.B Chair of Embedded Intelligence for Health Care and Wellbeing,University of Augsburg, Germany), Juan Liu(Huazhong University of Science and Technology), Huaiyuan Zheng(Huazhong University of Science and Technology), Wei Ji(Huazhong University of Science and Technology), Tomoya Koike(The University of Tokyo), Xiao Li(Children’s Hospital of Chongqing Medical University), Zixing Zhang(Imperial College London), Yoshiharu Yamamoto(The University of Tokyo) and Björn Schuller(University of Augsburg / Imperial College London)
Abstract: The COVID-19 outbreak was announced as a global pandemic by the World Health Organisation in March 2020 and has affected a growing number of people in the past few weeks. In this context, advanced artificial intelligence techniques are brought to the fore in responding to fight against and reduce the impact of this global health crisis. In this study, we focus on developing some potential use-cases of intelligent speech analysis for COVID-19 diagnosed patients. In particular, by analysing speech recordings from these patients, we construct audio-only-based models to automatically categorise the health state of patients from four aspects, including the severity of illness, sleep quality, fatigue, and anxiety. For this purpose, two established acoustic feature sets and support vector machines are utilised. Our experiments show that an average accuracy of .69 obtained estimating the severity of illness, which is derived from the number of days in hospitalisation. We hope that this study can foster an extremely fast, low-cost, and convenient way to automatically detect the COVID-19 disease.
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