VIBE: Voice-Induced open-ended Bias Evaluation for Large Audio-Language Models via Real-World Speech
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| Authors | Yi-Cheng Lin et al. |
| Year | 2026 |
| HF Upvotes | 1 |
| arXiv | 2604.17248 |
| Download | |
| HF Page | View on Hugging Face |
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.
Engineering Breakdown
The Problem
Large Audio-Language Models (LALMs) are increasingly integrated into daily applications, yet their generative biases remain underexplored.
The Approach
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.
Key Results
Evaluating 12 state-of-the-art LALMs reveals systematic biases in realistic scenarios.
Research Areas
This paper contributes to the following areas of AI/ML engineering:
- Machine learning
- Deep learning
- Neural networks
- Model optimization
- AI systems
- Voiceinduced
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