Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview
This paper addresses the challenge of diagnosing COVID-19 using computer audition, highlighting the current limitations and future directions for the artificial intelligence community.
This paper reviews recent advances in computer audition (CA) for diagnosing COVID-19 through speech and sound analysis. While encouraging milestones have been achieved, the authors conclude that no solid conclusions can yet be made due to sparse, unvalidated data and a lack of systematic comparisons with related respiratory diseases.
Computer audition (CA) has been demonstrated to be efficient in healthcare domains for speech-affecting disorders (e.g., autism spectrum, depression, or Parkinson's disease) and body sound-affecting abnormalities (e. g., abnormal bowel sounds, heart murmurs, or snore sounds). Nevertheless, CA has been underestimated in the considered data-driven technologies for fighting the COVID-19 pandemic caused by the SARS-CoV-2 coronavirus. In this light, summarise the most recent advances in CA for COVID-19 speech and/or sound analysis. While the milestones achieved are encouraging, there are yet not any solid conclusions that can be made. This comes mostly, as data is still sparse, often not sufficiently validated and lacking in systematic comparison with related diseases that affect the respiratory system. In particular, CA-based methods cannot be a standalone screening tool for SARS-CoV-2. We hope this brief overview can provide a good guidance and attract more attention from a broader artificial intelligence community.