COVYT: Introducing the Coronavirus YouTube and TikTok speech dataset featuring the same speakers with and without infection
This provides a balanced dataset for developing non-invasive COVID-19 detection tools, but it is incremental as it builds on existing computer audition methods by addressing data quality issues.
The researchers tackled the problem of limited data for AI-based COVID-19 detection from vocal sounds by introducing the COVYT dataset, which includes over 8 hours of speech from 65 speakers with both positive and negative samples, and they analyzed acoustic manifestations and classification scenarios to improve detection fairness.
More than two years after its outbreak, the COVID-19 pandemic continues to plague medical systems around the world, putting a strain on scarce resources, and claiming human lives. From the very beginning, various AI-based COVID-19 detection and monitoring tools have been pursued in an attempt to stem the tide of infections through timely diagnosis. In particular, computer audition has been suggested as a non-invasive, cost-efficient, and eco-friendly alternative for detecting COVID-19 infections through vocal sounds. However, like all AI methods, also computer audition is heavily dependent on the quantity and quality of available data, and large-scale COVID-19 sound datasets are difficult to acquire -- amongst other reasons -- due to the sensitive nature of such data. To that end, we introduce the COVYT dataset -- a novel COVID-19 dataset collected from public sources containing more than 8 hours of speech from 65 speakers. As compared to other existing COVID-19 sound datasets, the unique feature of the COVYT dataset is that it comprises both COVID-19 positive and negative samples from all 65 speakers. We analyse the acoustic manifestation of COVID-19 on the basis of these perfectly speaker characteristic balanced `in-the-wild' data using interpretable audio descriptors, and investigate several classification scenarios that shed light into proper partitioning strategies for a fair speech-based COVID-19 detection.