Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data
Jing Han, Chloë Brown, Jagmohan Chauhan, Andreas Grammenos, Apinan Hasthanasombat, Dimitris Spathis, Tong Xia, Pietro Cicuta
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.
BibTeX
@inproceedings{icassp2021_exploringautomat,
title = {Exploring Automatic COVID-19 Diagnosis via Voice and Symptoms from Crowdsourced Data},
author = {Jing Han and Chloë Brown and Jagmohan Chauhan and Andreas Grammenos and Apinan Hasthanasombat and Dimitris Spathis and Tong Xia and Pietro Cicuta and Cecilia Mascolo},
booktitle = {ICASSP 2021},
year = {2021}
}