# FairASR: Fair Audio Contrastive Learning for Automatic Speech Recognition

arXiv: https://arxiv.org/abs/2506.10747 · HTML: https://mlsanity.com/paper/2506.10747/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 12 Jun 2025.

## Built on by (most cited first, 3 of 3)

- [Acoustic-based Gender Differentiation in Speech-aware Language Models](https://mlsanity.com/paper/2509.21125/) (2025)
- [Where Do Self-Supervised Speech Models Become Unfair?](https://mlsanity.com/paper/2604.18249/) (2026)
- [Fairness Beyond a Single Run: Training-Seed Variability in Speech LLM Adaptation](https://mlsanity.com/paper/2609.38976/) (2026)

## Abstract (arXiv)

Large-scale ASR models have achieved remarkable gains in accuracy and robustness. However, fairness issues remain largely unaddressed despite their critical importance in real-world applications. In this work, we introduce FairASR, a system that mitigates demographic bias by learning representations that are uninformative about group membership, enabling fair generalization across demographic groups. Leveraging a multi-demographic dataset, our approach employs a gradient reversal layer to suppress demographic-discriminative features while maintaining the ability to capture generalizable speech patterns through an unsupervised contrastive loss. Experimental results show that FairASR delivers competitive overall ASR performance while significantly reducing performance disparities across different demographic groups.