FFASR: Benchmarking Far-Field Automatic Speech Recognition using High-Fidelity Simulated RIRs

Abstract:Far-field automatic speech recognition(ASR) degrades under reverberation, noise, and talker motion, yet the benchmarks that drive model selection emphasize close-microphone speech. We present FFASR, a held-out corpus of 15,637 utterances and an open leaderboard spanning nine conditions, each varying a single acoustic factor: anechoic near-field speech, a measured-versus-simulated office-lab pair, static far-field mixtures at high/mid/low signal-to-noise ratio(SNR), and moving-talker variants at matched SNR. Dry speech from 15 talkers is convolved with hybrid wave/geometrical-acoustics room impulse responses from 14 furnished rooms; because the speech is newly recorded and the test waveforms are never released, the corpus resists training-data contamination. Across contemporary systems, mean word error rate (WER) rises from 4.4% near-field to 41.3% in the static low-SNR condition; a moving talker adds a small but consistent penalty at matched SNR; and on the office-lab pair, measured and simulated WER agree to within about 1.7 pp on average. These results support high-fidelity simulation as a scalable proxy for measured far-field evaluation under the conditions we test.

Sound (cs.SD); Artificial Intelligence (cs.AI); Performance (cs.PF)

arXiv:2609.38897 [cs.SD]

(or arXiv:2609.38897v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2609.38897 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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