  The COVID-19 pandemic has led to a dramatic increase in the use of face masks
worldwide. Face coverings can affect both acoustic properties of the signal as
well as speech patterns and have unintended effects if the person wearing the
mask attempts to use speech processing technologies. In this paper we explore
the impact of wearing face masks on the automated assessment of English
language proficiency. We use a dataset from a large-scale speaking test for
which test-takers were required to wear face masks during the test
administration, and we compare it to a matched control sample of test-takers
who took the same test before the mask requirements were put in place. We find
that the two samples differ across a range of acoustic measures and also show a
small but significant difference in speech patterns. However, these differences
do not lead to differences in human or automated scores of English language
proficiency. Several measures of bias showed no differences in scores between
the two groups.
