Developing a Multi-variate Prediction Model For COVID-19 From Crowd-sourced Respiratory Voice Data

ArXi:2402.07619v2 Announce Type: replace-cross COVID-19 has affected than 223 countries worldwide and in the Post-COVID Era, there is a pressing need for non-invasive, low-cost, and highly scalable solutions to detect COVID-19. We develop a deep learning model to identify COVID-19 from voice recording data. The novelty of this work is in the development of deep learning models for COVID-19 identification from only voice recordings. We use the Cambridge COVID-19 Sound database which contains 893 speech samples, crowd-sourced from 4352 participants via a COVID-19 Sounds app.