VoiceNet has two human-annotated subsets.
VoiceNet tests recognition of nuanced emotions and speaking styles
The benchmark expands voice understanding beyond basic emotion labels, evaluating contrastive models on 40 emotions and 57 vocal attributes.
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Christoph Schuhmann · Robert Kaczmarczyk · Gollam Rabby · Felix Friedrich · Maurice Kraus · Gijs Wijngaard · +4 more
LAION e.V. · Scalable Learning & Multi-Purpose AI (SLAMPAI) Lab · Forschungszentrum Jülich GmbH · L3S Research Center · Black Forest Labs
Research Digest··2 min read
Thread:Voice Emotion Recognition
Schuhmann and colleagues introduce VoiceNet, a benchmark for recognizing fine-grained emotion and performance characteristics in permissively licensed, real-world speech.
Why this paper
From Black Forest Labs and 9 others
In one line
VoiceNet benchmarks fine-grained voice understanding with 40 emotions and 57 talking-style attributes, and VoiceCLAP models outperform prior CLAP baselines on it.
What we could check
- ·No code link found
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- ✓Limitations stated by the authors (3 noted)
- ✓Reports numbers on named benchmarks
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