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Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
- Publication Year :
- 2021
-
Abstract
- The development of fast and accurate screening tools, which could facilitate testing and prevent more costly clinical tests, is key to the current pandemic of COVID-19. In this context, some initial work shows promise in detecting diagnostic signals of COVID-19 from audio sounds. In this paper, we propose a voice-based framework to automatically detect individuals who have tested positive for COVID-19. We evaluate the performance of the proposed framework on a subset of data crowdsourced from our app, containing 828 samples from 343 participants. By combining voice signals and reported symptoms, an AUC of $0.79$ has been attained, with a sensitivity of $0.68$ and a specificity of $0.82$. We hope that this study opens the door to rapid, low-cost, and convenient pre-screening tools to automatically detect the disease.<br />Comment: 5 pages, 3 figures, 2 tables, Accepted for publication at ICASSP 2021
Details
- Database :
- arXiv
- Publication Type :
- Report
- Accession number :
- edsarx.2102.05225
- Document Type :
- Working Paper
- Full Text :
- https://doi.org/10.1109/ICASSP39728.2021.9414576