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The COVID-19 pandemic has led to a dramatic increase in the use of face masksworldwide. Face coverings can affect both acoustic properties of the signal aswell as speech patterns and have unintended effects if the person wearing themask attempts to use speech processing technologies. In this paper we explorethe impact of wearing face masks on the automated assessment of Englishlanguage proficiency. We use a dataset from a large-scale speaking test forwhich test-takers were required to wear face masks during the testadministration, and we compare it to a matched control sample of test-takerswho took the same test before the mask requirements were put in place. We findthat the two samples differ across a range of acoustic measures and also show asmall but significant difference in speech patterns. However, these differencesdo not lead to differences in human or automated scores of English languageproficiency. Several measures of bias showed no differences in scores betweenthe two groups.

Do face masks introduce bias in speech technologies? The case of automated scoring of speaking proficiency