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 accuracy is 35.5%. The strongest baseline, gemini_3_5_flash, reaches 46.5% on the seven-way task,...
| Authors | Luc Debaupte, Tyler Baumgartner, Brandon Tai et al. |
|---|---|
| Primary category | cs.CL |
| Published | 28 August 2026 |
| Last updated at source | 1 September 2026 |
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