# Towards Robust Dysarthric Speech Recognition: LLM-Agent Post-ASR Correction Beyond WER

arXiv: https://arxiv.org/abs/2601.21347 · HTML: https://mlsanity.com/paper/2601.21347/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jan 2026.

## Built on by (most cited first, 2 of 2)

- [Beyond Word Error Rate: Auditing the Diversity Tax in Speech Recognition through Dataset Cartography](https://mlsanity.com/paper/2603.05267/) (2026)
- [AI_LectureNote: A Retrospective Pilot Study of a Post-ASR Workflow for English-Script Rendering and Semantic Drift in Korean-English Medical Lectures](https://mlsanity.com/paper/2607.17237/) (2026)

## Abstract (arXiv)

While Automatic Speech Recognition (ASR) is typically benchmarked by word error rate (WER), real-world applications ultimately hinge on semantic fidelity. This mismatch is particularly problematic for dysarthric speech, where articulatory imprecision and disfluencies can cause severe semantic distortions. To bridge this gap, we introduce a Large Language Model (LLM)-based agent for post-ASR correction: a Judge-Editor over the top-k ASR hypotheses that keeps high-confidence spans, rewrites uncertain segments, and operates in both zero-shot and fine-tuned modes. In parallel, we release SAP-Hypo5, the largest benchmark for dysarthric speech correction, to enable reproducibility and future exploration. Under multi-perspective evaluation, our agent achieves a 14.51% WER reduction alongside substantial semantic gains, including a +7.59 pp improvement in MENLI and +7.66 pp in Slot Micro F1 on challenging samples. Our analysis further reveals that WER is highly sensitive to domain shift, whereas semantic metrics correlate more closely with downstream task performance.