论文标题
诊断Covid-19的计算机试镜的最新进展：概述
Recent Advances in Computer Audition for Diagnosing COVID-19: An Overview
论文作者
论文摘要
已经证明计算机试听（CA）在医疗保健领域有效，可用于言语疾病（例如自闭症，抑郁症或帕金森氏病）和身体声音异常（例如，肠声异常，默默声音或鼻音声音）。然而，CA在被认为是由SARS-COV-2冠状病毒引起的COVID-19型大流行的研究中被低估了CA。从这个角度来看，总结了CA的CA COVID-19语音和/或声音分析的最新进展。尽管所取得的里程碑令人鼓舞，但仍没有任何可靠的结论。这主要是因为数据仍然很稀疏，通常没有足够的验证，并且缺乏与影响呼吸系统的相关疾病的系统比较。特别是，基于CA的方法不能是SARS-COV-2的独立筛选工具。我们希望这个简短的概述可以提供良好的指导，并吸引更广泛的人工智能界的更多关注。
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.
