VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale
Expressive speech synthesis has outpaced expressive speech perception: systems now render fine-grained vocal performances that no public benchmark can score. Most benchmarks for this inverse problem stop at six to nine basic emotion categories, largely on acted speech. This paper introduces VoiceNet, a human-annotated representation-level benchmark for voice performance understanding on permissively-licensed in-the-wild speech. VoiceNet has two subsets: VoiceNet-Emo applies a 40-emotion taxonomy with three expert ratings per item, and VoiceNet-Ext, a preliminary subset, scores 57 talking-style attributes including speaking rate, vocal tension, breathiness, and register. The paper also rel...
| Authors | Christoph Schuhmann, Robert Kaczmarczyk, Gollam Rabby et al. |
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| Primary category | cs.SD |
| Published | 25 September 2026 |
| Last updated at source | 25 September 2026 |
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