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More than two years after its outbreak, the COVID-19 pandemic continues toplague medical systems around the world, putting a strain on scarce resources,and claiming human lives. From the very beginning, various AI-based COVID-19detection and monitoring tools have been pursued in an attempt to stem the tideof infections through timely diagnosis. In particular, computer audition hasbeen suggested as a non-invasive, cost-efficient, and eco-friendly alternativefor detecting COVID-19 infections through vocal sounds. However, like all AImethods, also computer audition is heavily dependent on the quantity andquality of available data, and large-scale COVID-19 sound datasets aredifficult to acquire -- amongst other reasons -- due to the sensitive nature ofsuch data. To that end, we introduce the COVYT dataset -- a novel COVID-19dataset collected from public sources containing more than 8 hours of speechfrom 65 speakers. As compared to other existing COVID-19 sound datasets, theunique feature of the COVYT dataset is that it comprises both COVID-19 positiveand negative samples from all 65 speakers. We analyse the acousticmanifestation of COVID-19 on the basis of these perfectly speakercharacteristic balanced `in-the-wild' data using interpretable audiodescriptors, and investigate several classification scenarios that shed lightinto proper partitioning strategies for a fair speech-based COVID-19 detection.

COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection