VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech

Yi-Cheng Lin; Yusuke Hirota; Sung-Feng Huang; Hung-yi Lee

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arXiv · 2604.17248

VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech

Yi-Cheng Lin·Yusuke Hirota·Sung-Feng Huang·Hung-yi Lee

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Abstract

Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored. Existing speech fairness benchmarks rely on synthetic speech and Multiple-Choice Questions (MCQs), both offering a fragmented view of fairness. We propose VIBE, a framework that evaluates generative bias through open-ended tasks such as personalized recommendations, using human-recorded speech. Unlike MCQs, our method allows stereotypical associations to manifest organically without predefined options, making it easily extensible to new tasks. Evaluating 12 state-of-the-art LALMs reveals systematic biases in realistic scenarios. Both gender and accent cues trigger statistically significant distributional shifts, and bias magnitude is strongly task-dependent.

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Yi-Cheng Lin, Yusuke Hirota, Sung-Feng Huang, Hung-yi Lee. 2026-04-19. VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech. https://arxiv.org/abs/2604.17248

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