SOURCE-LINKED INTELLIGENCE
VocalAffectBench: Evaluating Vocal Emotion Recognition in AI Audio Models
Voice products increasingly need affective cues that are present in speech but absent from transcripts. We introduce VocalAffectBench, a public, test-only benchmark for evaluating whether AI audio models can identify expressed vocal emotion from raw audio. The benchmark contains 273 human-recorded English WAV clips from 51 speaker accounts totaling 1.95 hours across seven labels: angry, disgusted, fearful, happy, neutral, sad, and surprised, with 39 clips per class. All baselines are evaluated from audio alone, without transcripts or contextual metadata. Across six released baselines, average
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Evidence & attribution
- arXiv · AI, language, vision and robotics · 2026-08-28T23:03:43.000Z
First collected: 2026-09-21T08:02:06.831Z. This is not the publication date.