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LLM ASR Correctors Damage Transcripts Without Audio Grounding

By PulseAugur Editorial · [1 sources] ·

A new research paper from arXiv analyzes the effectiveness of text-only Large Language Model (LLM) correctors for Automatic Speech Recognition (ASR) errors. The study found that when LLMs correct transcripts without access to the original audio, they can damage a significant percentage of already correct transcriptions. However, when the LLMs are provided with the audio context, the damage to correct transcripts is drastically reduced, though repair gains also decrease. The paper highlights that the aggregate Word Error Rate (WER) metric can obscure these repair-damage trade-offs, particularly in specialized domains like medical dictation. AI

IMPACT Highlights potential risks of using LLMs for transcription correction without full context, suggesting careful evaluation beyond aggregate metrics.

RANK_REASON Research paper published on arXiv analyzing LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM ASR Correctors Damage Transcripts Without Audio Grounding

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Research paper published on arXiv analyzing LLM performance. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Vasiliy Kudryavtsev, Kirill Borodin, Nikita Vasiliev, Mikhail Gorodnichev, Grach Mkrtchian ·

    Text-Only LLM Correctors Break Correct Transcripts: A Repair-Damage Analysis of Audio Grounding in ASR Error Correction

    arXiv:2610.30385v1 Announce Type: cross Abstract: LLM correctors of ASR output are judged by aggregate WER, which hides what they fix and what they break. We decompose every WER change exactly into repaired tokens, damaged tokens and insertions, and use this to ask what the sourc…

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