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

VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech: 0 upvotes on Hugging Face Daily Papers, #40 of 41 papers on 2026-07-08. Day-by-day upvote history.

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.

Paper page on Hugging Face · arXiv

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