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新冠疫情与计算机听觉:语音和声音分析在新冠病毒疫情中所能发挥作用的概述

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

作者信息

Schuller Björn W, Schuller Dagmar M, Qian Kun, Liu Juan, Zheng Huaiyuan, Li Xiao

机构信息

GLAM - Group on Language, Audio & Music, Imperial College London, London, United Kingdom.

EIHW - Chair of Embedded Intelligence for Health Care and Wellbeing, University of Augsburg, Augsburg, Germany.

出版信息

Front Digit Health. 2021 Mar 29;3:564906. doi: 10.3389/fdgth.2021.564906. eCollection 2021.

Abstract

At the time of writing this article, the world population is suffering from more than 2 million registered COVID-19 disease epidemic-induced deaths since the outbreak of the corona virus, which is now officially known as SARS-CoV-2. However, 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 COVID-19 directly or its symptoms such as 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 well-being. We quickly guide further through challenges that need to be faced for real-life usage and limitations also in comparison with non-audio solutions. 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.

摘要

在撰写本文时,自冠状病毒爆发以来,全球人口已因登记在案的COVID-19疾病流行导致超过200万人死亡,该病毒现正式称为严重急性呼吸综合征冠状病毒2(SARS-CoV-2)。然而,目前全球已做出巨大努力来应对和控制这场现已被列为大流行的疫情。在本论文中,我们概述了计算机听觉(CA)的潜力,即利用人工智能进行语音和声音分析以在这种情况下提供帮助。我们首先调查哪些相关或具有上下文意义的现象可以从语音或声音中自动评估。这些包括直接自动识别和监测COVID-19或其症状,如呼吸声、干咳和湿咳或打喷嚏声、感冒时的语音、进食行为、嗜睡或疼痛等等。然后,我们考虑潜在的应用案例。这些包括基于症状直方图及其随时间的发展进行风险评估和诊断,以及监测传播情况、社交距离及其影响、治疗和康复情况以及患者的健康状况。我们简要介绍了在实际应用中需要面对的挑战以及与非音频解决方案相比的局限性。我们得出的结论是,计算机听觉似乎已准备好实施(预)诊断和监测工具,并且更广泛地说,在抗击COVID-19传播方面提供了丰富且重要但尚未开发的潜力。

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