audio benchmark
VocalSound
A dataset for improving human vocal sounds recognition, containing over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Used for audio event classification and recognition of human non-speech vocalizations.
Updated Aug 11, 2026
Models1
Model coverage1
MetricScore
EvidenceB
VocalSound Ranking
Higher score ranks better on this benchmark.
| 01 | AC | 93.9% | 100.0% | 1 | C |
VocalSound Highlights
The leading models and scores on this benchmark.
What is VocalSound?
What VocalSound measures and how its scores work.
A dataset for improving human vocal sounds recognition, containing over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Used for audio event classification and recognition of human non-speech vocalizations.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- VocalSound
- Modality
- audio
- Primary category
- audio
- Score direction
- higher
- LLMBoard eligible
- No
- Evaluation key
- vocalsound|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about VocalSound.
Which model scores highest on VocalSound?
Qwen2.5-Omni-7B is currently ranked first with 93.9%.
What does VocalSound measure?
A dataset for improving human vocal sounds recognition, containing over 21,000 crowdsourced recordings of laughter, sighs, coughs, throat clearing, sneezes, and sniffs from 3,365 unique subjects. Used for audio event classification and recognition of human non-speech vocalizations.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
1 model results are currently shown.
Does this benchmark affect the overall score?
No. This benchmark is shown for reference but does not contribute to the overall score.