{"ID":2880202,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.15853","arxiv_id":"2508.15853","title":"MGSC: A Multi-granularity Consistency Framework for Robust End-to-end Asr","abstract":"End-to-end ASR models, despite their success on benchmarks, often pro-duce catastrophic semantic errors in noisy environments. We attribute this fragility to the prevailing 'direct mapping' objective, which solely penalizes final output errors while leaving the model's internal computational pro-cess unconstrained. To address this, we introduce the Multi-Granularity Soft Consistency (MGSC) framework, a model-agnostic, plug-and-play module that enforces internal self-consistency by simultaneously regulariz-ing macro-level sentence semantics and micro-level token alignment. Cru-cially, our work is the first to uncover a powerful synergy between these two consistency granularities: their joint optimization yields robustness gains that significantly surpass the sum of their individual contributions. On a public dataset, MGSC reduces the average Character Error Rate by a relative 8.7% across diverse noise conditions, primarily by preventing se-vere meaning-altering mistakes. Our work demonstrates that enforcing in-ternal consistency is a crucial step towards building more robust and trust-worthy AI.","short_abstract":"End-to-end ASR models, despite their success on benchmarks, often pro-duce catastrophic semantic errors in noisy environments. We attribute this fragility to the prevailing 'direct mapping' objective, which solely penalizes final output errors while leaving the model's internal computational pro-cess unconstrained. To...","url_abs":"https://arxiv.org/abs/2508.15853","url_pdf":"https://arxiv.org/pdf/2508.15853v1","authors":"[\"Xuwen Yang\"]","published":"2025-08-20T09:51:49Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.SD\",\"eess.AS\"]","methods":"[]","has_code":false}