Your Multimodal Speech Model Says I Have a Face for Radio

[2605.30472] Your Multimodal Speech Model Says I Have a Face for Radio

[Submitted on 28 May 2026]

Title:Your Multimodal Speech Model Says I Have a Face for Radio

View a PDF of the paper titled Your Multimodal Speech Model Says I Have a Face for Radio, by Maya K. Nachesa and Vlad Niculae and Vagrant Gautam

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Abstract:As large neural models have become better at language tasks, researchers are increasingly building multi- and omnimodal models that handle more modalities of data. One example is the expansion of speech recognition models to audio-visual data for noise mitigation and multimodal subtitling. While performance and bias have been studied extensively in the single-modality regime, it is unknown how new modalities affect this, even though they produce biases in humans. We therefore propose the first bias evaluation of multimodal speech recognition, where we create videos pairing different faces with the same audio, and measure changes in speech transcription accuracy. We find large quality-of-service differences across mWhisper-Flamingo and Gemini models, with drops of up to 4.05 word error rate points, across self-declared gender, ethnicity, and their intersection. Our findings point to a priority for developers to evaluate, fix, and communicate such limitations, as providing more signals through additional modalities is not necessarily better, and may even lead to biased outcomes.

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Computation and Language (cs.CL)

Cite as: arXiv:2605.30472 [cs.CL]

(or arXiv:2605.30472v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2605.30472

arXiv-issued DOI via DataCite (pending registration)

Submission history

From: Maya K. Nachesa [view email] [v1] Thu, 28 May 2026 18:42:38 UTC (78 KB)

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