  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.
