aiqbench analysis
This study challenges the assumption that scaling AI models automatically resolves bias, revealing that performance gaps between Mainstream US English and minority accents have remained stubborn or even worsened across systems released from 2021 to 2025. The findings highlight a systemic failure in current language technology infrastructure where standard benchmarks implicitly privilege dominant dialects, leading to significant degradation for underrepresented speaker populations.
Connecting the Dots: Enduring Performance Gaps in ASR Systems Between Standard and Minority English Varieties
arXiv:2610.17761v1 Announce Type: new Abstract: Automatic Speech Recognition (ASR) system evaluations have consistently revealed performance disparities along lines of race, gender, socioeconomic status, and language variety. While these disparities are well-documented, they are often (implicitly or explicitly) treated as issues that subsequent, larger, and more universal models will correct. We challenge this assumption of steady progress in reducing accent-related performance disparities, conducting a longitudinal evaluation of 16 prominent systems released between 2021-2025 across 8 datasets representing diverse English accent varieties. We find that the performance disparity between Mainstream US English (MUSE) and minority accents is stubborn, not reliably declining as a function of time. Specifically, while systems are reliably performant on MUSE, minority accents are subject to risks of large performance degradations between generations of systems, sometimes worsening by 65% absolute Word Error Rate. We argue that standard ASR benchmarks enact an implicit language policy privileging Mainstream US English varieties. As such, these performance disparities reflect systemic failures of language technology development infrastructure that disproportionately affect underrepresented speaker populations.
Abstract:Automatic Speech Recognition (ASR) system evaluations have consistently revealed performance disparities along lines of race, gender, socioeconomic status, and language variety. While these disparities are well-documented, they are often (implicitly or explicitly) treated as issues that subsequent, larger, and more universal models will correct. We challenge this assumption of steady progress in reducing accent-related performance disparities, conducting a longitudinal evaluation of 16 prominent systems released between 2021-2025 across 8 datasets representing diverse English accent varieties. We find that the performance disparity between Mainstream US English (MUSE) and minority accents is stubborn, not reliably declining as a function of time. Specifically, while systems are reliably performant on MUSE, minority accents are subject to risks of large performance degradations between generations of systems, sometimes worsening by 65% absolute Word Error Rate. We argue that standard ASR benchmarks enact an implicit language policy privileging Mainstream US English varieties. As such, these performance disparities reflect systemic failures of language technology development infrastructure that disproportionately affect underrepresented speaker populations.
Accepted to IEEE SLT 2026. 11 pages including appendix, 1 figure, 6 tables. Code and data: this https URL
Computation and Language (cs.CL)
arXiv:2610.17761 [cs.CL]
(or arXiv:2610.17761v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2610.17761 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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Full text under the publisher's open licence · 278 words
