Exploring Automatic COVID-19 Diagnosis via voice and symptoms from Crowdsourced Data

TL;DR

This paper presents a voice-based framework that combines audio signals and reported symptoms to automatically detect COVID-19, achieving promising accuracy on crowdsourced data for rapid pre-screening.

Contribution

It introduces a novel approach integrating voice analysis and symptom reports for COVID-19 detection, demonstrating effectiveness on real-world crowdsourced data.

Findings

Achieved an AUC of 0.79 in COVID-19 detection.

Sensitivity of 0.68 and specificity of 0.82.

Utilized 828 samples from 343 participants.

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.