audio benchmark
VocalSound Leaderboard
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 17, 2026
Models1
Model coverage1
MetricScore
EvidenceB
VocalSound Ranking
Higher score ranks better on this benchmark.
Sort by
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelAC | Score93.9% | Percentile100.0% | Participants1 | EvidenceC | Evaluated |
VocalSound Highlights
The leading models and scores on this benchmark.
The Top AI Models for VocalSound
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis vocalsound AI model leaderboard uses descending score in the benchmark's original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
- 01ACQwen2.5-Omni-7BAlibaba Cloud / Qwen Team
- Score
- 93.9%
- Price
- $0.10 input / $0.40 output per 1M tokens
Strengths
- Ranks #1 of 1 compared models
- 100th percentile on this benchmark
- C evidence result
Considerations
- This result measures VocalSound, not total model capability
Selection summary
Best AI Models for VocalSound
Qwen2.5-Omni-7B currently leads VocalSound with 93.9%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
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.