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.

本文研究语音识别中的不公平现象，通过划分发言者的群组并采取公平措施，比较利用地理和人口学信息与扩展的讲者嵌入技术的群组探测方法，发现在过采样和建模讲者群体成员方面均能缩小不同发言者群体间的差距并提高整体精确度。