[论文解读] COVID-19 and Computer Audition: An Overview on What Speech & Sound Analysis Could Contribute in the SARS-CoV-2 Corona Crisis

Björn W. Schuller, Dagmar Schuller+4 more|arXiv (Cornell University)|Mar 24, 2020

COVID-19 diagnosis using AI参考文献 56被引用 66

一句话总结

本文综述了计算听觉在新冠肺炎中的应用，通过分析语言和声音来识别症状、接近度、监测和潜在诊断，并讨论挑战与伦理问题。

ABSTRACT

At the time of writing, the world population is suffering from more than 10,000 registered COVID-19 disease epidemic induced deaths since the outbreak of the Corona virus more than three months ago now officially known as SARS-CoV-2. Since, tremendous efforts have been made worldwide to counter-steer and control the epidemic by now labelled as pandemic. In this contribution, we provide an overview on the potential for computer audition (CA), i.e., the usage of speech and sound analysis by artificial intelligence to help in this scenario. We first survey which types of related or contextually significant phenomena can be automatically assessed from speech or sound. These include the automatic recognition and monitoring of breathing, dry and wet coughing or sneezing sounds, speech under cold, eating behaviour, sleepiness, or pain to name but a few. Then, we consider potential use-cases for exploitation. These include risk assessment and diagnosis based on symptom histograms and their development over time, as well as monitoring of spread, social distancing and its effects, treatment and recovery, and patient wellbeing. We quickly guide further through challenges that need to be faced for real-life usage. We come to the conclusion that CA appears ready for implementation of (pre-)diagnosis and monitoring tools, and more generally provides rich and significant, yet so far untapped potential in the fight against COVID-19 spread.

研究动机与目标

• 推动将计算听觉（CA）应用于COVID-19危机。
• 识别CA可以自动评估的与COVID-19相关的音频现象和信号。
• 概述风险评估、诊断、监测与康复的潜在用例。
• 突出现实世界CA部署在技术、伦理与社会方面的挑战。

提出的方法

• 调查现有的CA任务，如语音分析、呼吸与咳嗽声音识别、口罩检测，以及情感/睡眠/疼痛评估。
• 将CA能力映射到COVID-19的用例，如风险评估、通过症状直方图进行诊断、传播监测以及治疗/康复。
• 讨论3D定位、分段、声源分离和降噪等作为公共场所和临床环境的辅助工具。
• 提出生成语音和声音以帮助在COVID-19情境中的沟通与警报系统。

实验结果

研究问题

• RQ1 目前有哪些与COVID-19症状和情况相关的CA任务？
• RQ2 在COVID-19情境中，CA任务如何用于风险评估、诊断、监测和治疗？
• RQ3 在现实世界的COVID-19情景中部署CA面临的主要挑战与伦理考量是什么？

主要发现

• CA可以自动评估呼吸、咳嗽（干/湿）、打喷嚏、戴口罩情境下的语言、呼吸模式与嗜睡等信号。
• 潜在用例包括风险评估、通过随时间的症状直方图进行的基于音频的诊断、传播监测与社交距离，以及治疗/康复监测。
• CA技术如说话人计数、分段、接近检测和源分离可以支持公共场所和临床环境的监测。
• 生成语音和声音可以帮助在COVID-19情境中的沟通与警报系统。