CARES: A Controlled Synthetic Benchmark of Speaker Reactions to Sound

Abstract:Automatic audio scene description turns a recording into a text account of a situation. One difficulty is deciding which elements of the audio should be kept, since a description cannot include them all. Annotators disagree about this, making a ground truth hard to obtain. In this work, we first define the ground truth, then generate the data. We focus on audio events and define sound salience with a simple rule: a sound is salient when a speaker audibly reacts to it. For scale and variety, a controlled set of scenarios fixes the ground truth, and a language model writes the dialogues. The resulting corpus, CARES, contains 10,000 two-speaker scenes. We then benchmark six audio-language models on three tasks: identifying the scene, tagging the sounds present, and classifying reactions. We show that these models hear the sounds but miss how the speakers react to them.

Sound (cs.SD); Machine Learning (cs.LG)

arXiv:2610.10208 [cs.SD]

(or arXiv:2610.10208v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2610.10208 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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