  As for other forms of AI, speech recognition has recently been examined with
respect to performance disparities across different user cohorts. One approach
to achieve fairness in speech recognition is to (1) identify speaker cohorts
that suffer from subpar performance and (2) apply fairness mitigation measures
targeting the cohorts discovered. In this paper, we report on initial findings
with both discovery and mitigation of performance disparities using data from a
product-scale AI assistant speech recognition system. We compare cohort
discovery based on geographic and demographic information to a more scalable
method that groups speakers without human labels, using speaker embedding
technology. For fairness mitigation, we find that oversampling of
underrepresented cohorts, as well as modeling speaker cohort membership by
additional input variables, reduces the gap between top- and bottom-performing
cohorts, without deteriorating overall recognition accuracy.
