Voice AI Benchmarking
Voice AI benchmarking with real human audio data
Besimple builds benchmark datasets that expose where speech and audio models fail in production workflows, from exact structured tokens to vocal affect.
- Public releases
- 2
- Voice Code Bench
- 300
- Vocal Affect Bench
- 280
Benchmark design
Define tasks, speaker coverage, evaluation criteria, and release artifacts around real production failure modes.
Dataset documentation
Document provenance, collection regime, speaker metadata, quality review, and known limitations so results are interpretable.
Repeatable scoring
Package audio, metadata, scoring scripts, and reports so teams can compare model revisions over time.
Use Cases
Where this helps
- Evaluate ASR systems on exact structured values that WER can hide.
- Measure whether audio models detect vocal emotion from raw speech.
- Create private regression sets for voice-agent workflows.
- Publish research artifacts that are credible to model builders and customers.