  The airborne transmission of disease-causing pathogens like the SARS-CoV-2
virus responsible for the COVID-19 pandemic is known to occur not only through
violent expiratory events like coughs or sneezes, but also during activities
like singing and even speech. We study, using direct numerical simulation
(DNS), the transport of virus-laden aerosols generated during human speech. We
show that the use of the "top-hat" formulation for steady speech flows can
underestimate infection probabilities by a factor of 2-3. We propose an
alternative method of estimating viral ingestion that includes exposure through
the eyes and mouth, to provide more realistic estimates of the risk of
infection. This method is then used to determine infection probabilities when
two speakers facing each other are engaged in a short conversation. We show
that the interaction between the two oppositely-directed speech jets can
increase or decrease infection probabilities, which peak for a height
difference between the speakers of about 10cm (30cm) when the axial separation
between them is 2ft (6ft). These results can help improve the existing public
health guidelines for COVID mitigation by introducing a lateral shift between
people during short conversations. Moreover, simulations like these can provide
useful inputs to the existing epidemiological models for improving their
reliability and accuracy.
