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Speech as a Biomarker for COVID-19 Detection Using Machine Learning
Mohammed Usman1, Vinit Kumar Gunjan2, Mohd Wajid3
1Department of Electrical Engineering, King Khalid University, Abha 61411, Saudi Arabia.
Computational Intelligence and Neuroscience
|April 21, 2022
Summary
Speech analysis reveals distinct statistical changes in spectral features for COVID-19 detection. Machine learning models accurately identify positive cases, with Decision Forest showing the highest recall for diagnosing this respiratory illness.
Area of Science:
- Biomedical Signal Processing
- Machine Learning Applications
- Respiratory Illness Diagnostics
Background:
- Physiological changes associated with COVID-19 can alter speech characteristics.
- Speech spectral features, analyzed via Short-Time Fourier Transform (STFT), offer potential biomarkers.
- Distinguishing between healthy and COVID-19 positive individuals using speech is an emerging diagnostic approach.
Purpose of the Study:
- To investigate the efficacy of speech spectral features for COVID-19 diagnosis.
- To apply and evaluate machine learning classification algorithms for identifying COVID-19 positive individuals.
- To optimize models for minimizing misclassification of positive cases.
Main Methods:
- Statistical analysis of speech spectral features derived from STFT.
- Utilizing speech samples from both healthy and asymptomatic COVID-19 positive individuals.
- Training and evaluating five state-of-the-art machine learning classifiers, including Decision Forest.
Main Results:
- Higher Root Mean Square (RMS) error in statistical distribution fitting for COVID-19 positive speech samples compared to healthy samples.
- Performance evaluation of multiple machine learning algorithms, with parameter tuning to prioritize correct identification of positive cases.
- Decision Forest algorithm achieved the highest recall of 0.7892 in classifying COVID-19 positive individuals.
Conclusions:
- Speech spectral analysis, combined with machine learning, demonstrates potential for non-invasive COVID-19 detection.
- The study highlights the statistical differences in speech patterns between infected and healthy individuals.
- Optimized machine learning models, particularly Decision Forest, can effectively aid in the early identification of COVID-19.
