VIBE is a new framework designed to evaluate generative bias in Large Audio-Language Models (LALMs) using human-recorded speech rather than synthetic data. By utilizing open-ended tasks like personalized recommendations, it overcomes the limitations of traditional Multiple-Choice Question benchmarks to better identify stereotypical associations. This approach provides a more comprehensive assessment of fairness in real-world speech applications.