COVID-19 Detection from Speech in Noisy Conditions

Abstract

We explore the integration of audio enhancement into a speech-based COVID-19 detection system in an attempt to make speech captured in noisy environments from everyday life useful for the detection of the virus. For this purpose, two multi-task learning approaches are exploited to jointly optimise a front-end speech enhancement model and a subsequent COVID-19 detection model. In comparison to several baseline methods, such as noisy data augmentation, cold cascade of speech enhancement, and COVID-19 models, our proposed solutions are able to recover a substantial percentage of the performance reduction caused by real-world noises. Our best-performing model, which is trained using the synthetic data of the DiCOVA speech corpus and AudioSet environmental backgrounds, can achieve an average AUC of 76.87 % on the test data covering a wide range of noise intensities, which is over 10 % better than a COVID-19 model trained with clean audio.