Sharma Neeraj Kumar, Muguli Ananya, Krishnan Prashant, Kumar Rohit, Chetupalli Srikanth Raj, Ganapathy Sriram
Learning and Extraction of Acoustic Patterns (LEAP) Lab, Electrical Engineering, Indian Institute of Science, Bangalore, India.
Comput Speech Lang. 2022 May;73:101320. doi: 10.1016/j.csl.2021.101320. Epub 2021 Nov 24.
The technology development for point-of-care tests (POCTs) targeting respiratory diseases has witnessed a growing demand in the recent past. Investigating the presence of acoustic biomarkers in modalities such as cough, breathing and speech sounds, and using them for building POCTs can offer fast, contactless and inexpensive testing. In view of this, over the past year, we launched the "Coswara" project to collect cough, breathing and speech sound recordings via worldwide crowdsourcing. With this data, a call for development of diagnostic tools was announced in the Interspeech 2021 as a special session titled "Diagnostics of COVID-19 using Acoustics (DiCOVA) Challenge". The goal was to bring together researchers and practitioners interested in developing acoustics-based COVID-19 POCTs by enabling them to work on the same set of development and test datasets. As part of the challenge, datasets with breathing, cough, and speech sound samples from COVID-19 and non-COVID-19 individuals were released to the participants. The challenge consisted of two tracks. The Track-1 focused only on cough sounds, and participants competed in a leaderboard setting. In Track-2, breathing and speech samples were provided for the participants, without a competitive leaderboard. The challenge attracted 85 plus registrations with 29 final submissions for Track-1. This paper describes the challenge (datasets, tasks, baseline system), and presents a focused summary of the various systems submitted by the participating teams. An analysis of the results from the top four teams showed that a fusion of the scores from these teams yields an area-under-the-receiver operating curve (AUC-ROC) of 95.1% on the blind test data. By summarizing the lessons learned, we foresee the challenge overview in this paper to help accelerate technological development of acoustic-based POCTs.
针对呼吸道疾病的即时检验（POCT）技术开发在最近需求不断增长。研究咳嗽、呼吸和语音等模态中声学生物标志物的存在情况，并将其用于构建即时检验，可以提供快速、非接触且廉价的检测。有鉴于此，在过去一年里，我们启动了“Coswara”项目，通过全球众包收集咳嗽、呼吸和语音录音。利用这些数据，在2021年国际语音会议上宣布了一项开发诊断工具的呼吁，作为一个名为“利用声学诊断COVID-19（DiCOVA）挑战赛”的特别会议。目标是通过让对开发基于声学的COVID-19即时检验感兴趣的研究人员和从业者使用同一组开发和测试数据集，将他们聚集在一起。作为挑战赛的一部分，向参与者发布了来自COVID-19和非COVID-19个体的呼吸、咳嗽和语音样本数据集。挑战赛包括两个赛道。赛道1仅关注咳嗽声音，参与者在排行榜设置中竞争。在赛道2中，向参与者提供呼吸和语音样本，但没有竞争性排行榜。该挑战赛吸引了85多个注册者，赛道1有29份最终提交作品。本文描述了挑战赛（数据集、任务、基线系统），并对参赛团队提交的各种系统进行了重点总结。对排名前四的团队的结果分析表明，这些团队分数的融合在盲测数据上产生的受试者操作特征曲线下面积（AUC-ROC）为95.1%。通过总结经验教训，我们预计本文中的挑战赛概述将有助于加速基于声学的即时检验的技术开发。
