Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities

• URL: http://arxiv.org/abs/2207.11345v1
• Date: Fri, 22 Jul 2022 21:33:29 GMT
• Title: Toward Fairness in Speech Recognition: Discovery and mitigation of performance disparities
• Authors: Pranav Dheram, Murugesan Ramakrishnan, Anirudh Raju, I-Fan Chen, Brian King, Katherine Powell, Melissa Saboowala, Karan Shetty, Andreas Stolcke
• Abstract summary: We report on initial findings with both discovery and mitigation of performance disparities using data from a product-scale AI assistant speech recognition system. 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.
• Score: 10.917512121301135
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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