COVID-19下语音智能分析的早期研究：严重程度、睡眠质量、疲劳和焦虑
An Early Study on Intelligent Analysis of Speech under COVID-19: Severity, Sleep Quality, Fatigue, and Anxiety
摘要：
世界卫生组织于二零二零年三月宣布COVID-19爆发为全球大流行病，并于过去数周影响越来越多人。在这种情况下，先进的人工智能技术在应对和减少这场全球健康危机的影响方面发挥了重要作用。在这项研究中，我们专注于为COVID-19诊断患者开发智能语音分析的一些潜在用例。特别是，通过分析这些患者的语音记录，我们构建了仅基于音频的模型，从四个方面自动分类患者的健康状态，包括疾病的严重程度，睡眠质量，疲劳和焦虑。为此，两个建立的声学特征集和支持向量机被利用。我们的实验表明，估计疾病严重程度的平均准确度为0.69，这是从住院天数中得出的。我们希望这项研究能够促进一种非常快速，低成本和方便的方法来自动检测COVID-19疾病。
英文摘要：
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.
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。
