# Detection of COVID-19 through the analysis of vocal fold oscillations

arXiv: https://arxiv.org/abs/2010.10707 · HTML: https://mlsanity.com/paper/2010.10707/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Oct 2020.

## Built on by (most cited first, 10 of 10)

- [The INTERSPEECH 2021 Computational Paralinguistics Challenge: COVID-19 Cough, COVID-19 Speech, Escalation & Primates](https://mlsanity.com/paper/2102.13468/) (2021)
- [Sounds of COVID-19: exploring realistic performance of audio-based digital testing](https://mlsanity.com/paper/2106.15523/) (2021)
- [Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets](https://mlsanity.com/paper/2205.13607/) (2022)
- [Exploring Longitudinal Cough, Breath, and Voice Data for COVID-19 Progression Prediction via Sequential Deep Learning: Model Development and Validation](https://mlsanity.com/paper/2201.01232/) (2022)
- [An Overview of Techniques for Biomarker Discovery in Voice Signal](https://mlsanity.com/paper/2110.04678/) (2021)
- [Automatic COVID-19 disease diagnosis using 1D convolutional neural network and augmentation with human respiratory sound based on parameters: cough, breath, and voice](https://mlsanity.com/paper/2112.07285/) (2021)
- [Detection of Disease on Nasal Breath Sound by New Lightweight Architecture: Using COVID-19 as An Example](https://mlsanity.com/paper/2504.00730/) (2025)
- [On the pragmatism of using binary classifiers over data intensive neural network classifiers for detection of COVID-19 from voice](https://mlsanity.com/paper/2204.04802/) (2022)
- [Fused Audio Instance and Representation for Respiratory Disease Detection](https://mlsanity.com/paper/2204.10581/) (2022)
- [Optimising MFCC parameters for the automatic detection of respiratory diseases](https://mlsanity.com/paper/2408.07522/) (2024)

## Abstract (arXiv)

Phonation, or the vibration of the vocal folds, is the primary source of vocalization in the production of voiced sounds by humans. It is a complex bio-mechanical process that is highly sensitive to changes in the speaker's respiratory parameters. Since most symptomatic cases of COVID-19 present with moderate to severe impairment of respiratory functions, we hypothesize that signatures of COVID-19 may be observable by examining the vibrations of the vocal folds. Our goal is to validate this hypothesis, and to quantitatively characterize the changes observed to enable the detection of COVID-19 from voice. For this, we use a dynamical system model for the oscillation of the vocal folds, and solve it using our recently developed ADLES algorithm to yield vocal fold oscillation patterns directly from recorded speech. Experimental results on a clinically curated dataset of COVID-19 positive and negative subjects reveal characteristic patterns of vocal fold oscillations that are correlated with COVID-19. We show that these are prominent and discriminative enough that even simple classifiers such as logistic regression yields high detection accuracies using just the recordings of isolated extended vowels.