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

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.

arXiv comment5 pages, 3 figures, 2 tables, Accepted for publication at ICASSP 2021

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Cited by in corpus (10)

• From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
• Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation
• Supervised and Self-supervised Pretraining Based COVID-19 Detection Using Acoustic Breathing/Cough/Speech Signals
• Transfer Learning for the Efficient Detection of COVID-19 from Smartphone Audio Data
• A Cough-based deep learning framework for detecting COVID-19
• The Second DiCOVA Challenge: Dataset and performance analysis for COVID-19 diagnosis using acoustics
• Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data
• Uncertainty-Aware COVID-19 Detection from Imbalanced Sound Data
• Voice2Mesh: Cross-Modal 3D Face Model Generation from Voices
• Using Self-Supervised Feature Extractors with Attention for Automatic COVID-19 Detection from Speech