COVID-19 Detection via Fusion of Modulation Spectrum and Linear Prediction Speech Features
IEEE/ACM Transactions on Audio Speech and Language Processing
; : 1-14, 2023.
Article
in English
| Scopus | ID: covidwho-2306621
ABSTRACT
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 48h), 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 test 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 and design a two-stage COVID-19 prediction system by fusing the proposed features. Experiments with three COVID-19 speech datasets (CSS, DiCOVA2, and Cambridge subset) show that the two-stage feature fusion system outperforms the benchmark systems of CSS and Cambridge datasets while maintaining lower complexity compared to DL-based systems. Furthermore, the two-stage system demonstrates higher generalizability to unseen conditions in a cross-dataset testing evaluation scheme. The generalizability and interpretability of our proposed system demonstrate the potential for accessible, low-cost, at-home COVID-19 testing. IEEE
COVID-19; COVID-19 diagnostics; Diseases; Feature extraction; Frequency modulation; linear predictive analysis; Modulation; modulation spectrogram; Spectrogram; speech analysis; Time-frequency analysis; Diagnosis; Forecasting; Spectrographs; Speech recognition; Statistical tests; Viruses; Cambridge; Coronavirus disease 2019 diagnostic; Features extraction; Linear prediction; Modulation spectrum; Spectrograms
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
ACM Transactions on Audio Speech and Language Processing
Year:
2023
Document Type:
Article
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