Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data
This work addresses the need for rapid, low-cost pre-screening tools for COVID-19 detection, though it is incremental as it builds on initial audio-based detection methods.
The paper tackled COVID-19 diagnosis by developing a voice-based framework that combines voice signals and reported symptoms, achieving an AUC of 0.79, sensitivity of 0.68, and specificity of 0.82 on crowdsourced data.
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.