The DiCOVA challenge aims at accelerating research in diagnosing COVID-19 using acoustics (DiCOVA), a topic at the intersection of speech and audio processing, respiratory health diagnosis, and machine learning. This challenge is an open call for researchers to analyze a dataset of sound recordings collected from COVID-19 infected and non-COVID-19 individuals for a two-class classification. These recordings were collected via crowdsourcing from multiple countries, through a website application. The challenge features two tracks, one focuses on using cough sounds, and the other on using a collection of breath, sustained vowel phonation, and number counting speech recordings. In this paper, we introduce the challenge and provide a detailed description of the dataset, task, and present a baseline system for the task.

翻译：DiCOVA挑战旨在加速对使用声学(DiCOVA)诊断COVID-19的研究,这是一个语言和声学处理、呼吸卫生诊断和机器学习交叉点的主题,这是一项公开呼吁,研究人员应分析从COVID-19感染者和非COVID-19个人收集的录音数据集,以进行两级分类,这些录音是通过网站应用程序从多个国家通过众包收集的,有两个轨道,一个轨道是咳嗽声,另一个轨道是使用呼吸、持续共鸣和数和数数数录音集,我们在此文件中提出挑战,并详细说明数据集、任务和任务的基准系统。