Mini Review ARTICLE
COVID-19 and Computer Audition:An Overview on What Speech & SoundAnalysis Could Contribute in theSARS-CoV-2 Corona Crisis
- 1Imperial College London, United Kingdom
- 2University of Augsburg, Germany
- 3audEERING GmbH, Germany
- 4The University of Tokyo, Japan
- 5Huazhong University of Science and Technology, China
- 6Chongqing Medical University, China
At the time of writing, the world population is suffering from more than two million registered COVID-19 disease epidemic induced deaths since the outbreak of the Corona virus 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 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 wellbeing. We quickly guide further through challenges that need to be faced for real-life usage and limitations also comparing 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.
Keywords: Corona virus, SARS-CoV-2, COVID-19, computer audition, Machine listening, Computational paralinguistics
Received: 22 May 2020;
Accepted: 03 Feb 2021.
Copyright: © 2021 Schuller, Schuller, Qian, Liu, Zheng and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Prof. Björn W. Schuller, Imperial College London, London, United Kingdom, email@example.com