Deshpande Gauri, Batliner Anton, Schuller Björn W
Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Germany.
TCS Research Pune, India.
Pattern Recognit. 2022 Feb;122:108289. doi: 10.1016/j.patcog.2021.108289. Epub 2021 Aug 30.
The Coronavirus (COVID-19) pandemic impelled several research efforts, from collecting COVID-19 patients' data to screening them for virus detection. Some COVID-19 symptoms are related to the functioning of the respiratory system that influences speech production; this suggests research on identifying markers of COVID-19 in speech and other human generated audio signals. In this article, we give an overview of research on human audio signals using 'Artificial Intelligence' techniques to screen, diagnose, monitor, and spread the awareness about COVID-19. This overview will be useful for developing automated systems that can help in the context of COVID-19, using non-obtrusive and easy to use bio-signals conveyed in human non-speech and speech audio productions.
冠状病毒(COVID-19)大流行推动了多项研究工作,从收集COVID-19患者的数据到对他们进行病毒检测筛查。一些COVID-19症状与影响言语产生的呼吸系统功能有关;这表明有必要开展研究,以识别言语和其他人类生成的音频信号中的COVID-19标志物。在本文中,我们概述了利用“人工智能”技术对人类音频信号进行研究,以筛查、诊断、监测COVID-19并提高其认知度。这一概述将有助于开发自动化系统,这些系统可利用人类非言语和言语音频产生中传达的非侵入性且易于使用的生物信号,在COVID-19背景下发挥作用。