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arXiv · 2510.18374

Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning

Monorama Swain·Bubai Maji·Jagabandhu Mishra·Markus Schedl·Anders Søgaard·Jesper Rindom Jensen
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Abstract

In this work, we address the challenge of building fair English ASR systems for second-language speakers. Our analysis of widely used ASR models, Whisper and Seamless-M4T, reveals large fluctuations in word error rate (WER) across 26 accent groups, indicating significant fairness gaps. To mitigate this, we propose fairness-prompted finetuning with lightweight adapters, incorporating Spectral Decoupling (SD), Group Distributionally Robust Optimization (Group-DRO), and Invariant Risk Minimization (IRM). Our proposed fusion of traditional empirical risk minimization (ERM) with cross-entropy and fairness-driven objectives (SD, Group DRO, and IRM) enhances fairness across accent groups while maintaining overall recognition accuracy. In terms of macro-averaged word error rate, our approach achieves a relative improvement of 58.7% and 58.5% over the large pretrained Whisper and SeamlessM4T, and 9.7% and 7.8% over them, finetuning with standard empirical risk minimization with cross-entropy loss.

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Monorama Swain, Bubai Maji, Jagabandhu Mishra, Markus Schedl, Anders Søgaard, Jesper Rindom Jensen. 2025-10-21. Towards Fair ASR For Second Language Speakers Using Fairness Prompted Finetuning. https://arxiv.org/abs/2510.18374

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