InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR
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Computer Science > Computation and Language
arXiv:2610.08604 (cs)
[Submitted on 6 Oct 2026]
Title:InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR
View a PDF of the paper titled InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR, by Ashley E. Bravo-Bravo and 4 other authors
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Abstract:Automatic Speech Recognition (ASR) systems often show uneven performance across demographic groups, and errors can be especially difficult to address for speakers belonging to multiple demographic groups. This work studies demographic-aware model merging for fair Speech-LLM-based ASR. Starting from a SLAM-ASR-based model, we fine-tune only the connector on demographic-specific subsets and merge the resulting subgroup-adapted connectors into a global model. We then identify critical cross-axis demographic pairs using subgroup WER and task-vector conflict, and apply intersection-specific correction vectors to the global merged model. Experiments on Fair-Speech show that global demographic merging improves overall WER over the base model, while intersection correction provides additional gains for several merging strategies. In particular, TIES with WER-based correction achieves the best overall WER, reducing it from 7.38\% to 5.13\%. Subgroup and disparity analyses further show that the proposed approach improves performance across demographic axes, while highlighting that lower average WER does not always imply reduced subgroup disparity.
| Comments: | Under Review |
| Subjects: | Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2610.08604 [cs.CL] |
| (or arXiv:2610.08604v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08604
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled InterCorrect: Intersection-Aware Correction of Demographic Model Merging for Fair ASR, by Ashley E. Bravo-Bravo and 4 other authors
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