An Early Study on Intelligent Analysis of Speech Under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
| Autor: | Juan Liu, Zhao Ren, Shuo Liu, Kun Qian, Huaiyuan Zheng, Zixing Zhang, Yoshiharu Yamamoto, Björn Schuller, Meishu Song, Xiao Li, Zijiang Yang, Wei Ji, Tomoya Koike, Jing Han |
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| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Coronavirus disease 2019 (COVID-19) Computer science Applied psychology 050801 communication & media studies Context (language use) 02 engineering and technology Disease Computer Science - Sound 0508 media and communications Audio and Speech Processing (eess.AS) Severity of illness Pandemic FOS: Electrical engineering electronic engineering information engineering 0202 electrical engineering electronic engineering information engineering medicine Global health Computer Science - Computation and Language 05 social sciences Anxiety 020201 artificial intelligence & image processing ddc:004 medicine.symptom Construct (philosophy) Computation and Language (cs.CL) Electrical Engineering and Systems Science - Audio and Speech Processing |
| Zdroj: | INTERSPEECH |
| DOI: | 10.21437/interspeech.2020-2223 |
| Popis: | 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 © 2020 ISCA |
| Databáze: | OpenAIRE |
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