COVID-19 Detection via Fusion of Modulation Spectrum and Linear Prediction Speech Features.

The coronavirus disease 2019 (COVID-19) pandemic has drastically impacted life around the globe. As life returns to pre-pandemic routines, COVID-19 testing has become a key component, assuring that travellers and citizens are free from the disease. Conventional tests can be expensive, time-consuming (results can take up to 48 h), and require laboratory testing. Rapid antigen testing, in turn, can generate results within 15-30 minutes and can be done at home, but research shows they achieve very poor sensitivity rates. In this paper, we propose an alternative based on speech signals recorded at home with a portable device. It has been well-documented that the virus affects many of the speech production systems (e.g., lungs, larynx, and articulators). As such, we propose the use of new modulation spectral features and linear prediction analysis to characterize these changes via a two-stage classification system. Experiments on three COVID-19 speech datasets show that the proposed two-stage system outperforms several state-of-the-art benchmarks, relies on interpretable features, as well as generalizes well to unseen datasets. Overall, the proposed system shows promise as an accessible, low-cost, at-home method for COVID-19 detection.