asr far-field-audio benchmark acoustic-robustness voice-agents

Open far-field ASR benchmark measures real acoustic gaps

FFASR Leaderboard quantifies WER degradation across 14 simulated rooms with sim-to-real validation, replacing proprietary evaluation pipelines with reproducible far-field metrics.

Summary

Models scoring well on clean-speech benchmarks often degrade substantially in deployment. This leaderboard exposes acoustic robustness gaps and speed-accuracy tradeoffs that matter for voice agents, robotics, and in-car systems before production.

Why it matters

Implementation verdict

Ready now. Submit your ASR model to Hugging Face leaderboard at https://huggingface.co/spaces/treble-technologies/ffasr. Requires inference on NVIDIA L4 GPU under standardized conditions. Replaces ad-hoc far-field testing with reproducible ranking. Moving-source evaluation still in beta; multi-talker scenarios coming later.

Sources

1.far-field WER at low SNR is consistently several times higher than near-field WER on the same speech content
2.The gap between benchmark performance and real-world deployment is one of the more persistent frustrations in ASR development
3.hybrid wave-based simulation, sim-to-real validation, moving-source splits in beta, held-out audio, and standardized evaluation hardware across all submissions
4.Fourteen fully furnished rooms are included in the benchmark, ranging from 20 to 470 m³
5.the Pareto front view in the Analysis tab makes that tradeoff explicit

Dev Signal

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