  In the pathogenesis of COVID-19, impairment of respiratory functions is often
one of the key symptoms. Studies show that in these cases, voice production is
also adversely affected -- vocal fold oscillations are asynchronous,
asymmetrical and more restricted during phonation. This paper proposes a method
that analyzes the differential dynamics of the glottal flow waveform (GFW)
during voice production to identify features in them that are most significant
for the detection of COVID-19 from voice. Since it is hard to measure this
directly in COVID-19 patients, we infer it from recorded speech signals and
compare it to the GFW computed from physical model of phonation. For normal
voices, the difference between the two should be minimal, since physical models
are constructed to explain phonation under assumptions of normalcy. Greater
differences implicate anomalies in the bio-physical factors that contribute to
the correctness of the physical model, revealing their significance indirectly.
Our proposed method uses a CNN-based 2-step attention model that locates
anomalies in time-feature space in the difference of the two GFWs, allowing us
to infer their potential as discriminative features for classification. The
viability of this method is demonstrated using a clinically curated dataset of
COVID-19 positive and negative subjects.
