  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.
