Papers with audio question answering benchmarks

1 papers
AUDITA: A New Dataset to Audit Humans vs. AI Skill at Audio QA (2026.findings-acl)

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Challenge: Existing audio question answering benchmarks emphasize sound event classification or caption-grounded queries.
Approach: They propose a large-scale, real-world audio question answering benchmark to evaluate audio reasoning beyond surface-level acoustic recognition.
Outcome: The proposed model achieves 32.13% accuracy while demonstrating comprehension of audio . state-of-the-art models perform poorly, with average accuracy below 8.86%.

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