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

Large language models can post-edit ASR by using linguistic context to compare N-best hypotheses and rewrite the transcript when needed, often improving WER without retraining the recognizer. However, prompt-only correction is brittle: it may over-edit when top-1 is correct and hallucinate when the reference is absent, and often needs very large models. We propose oracle-guided fine-tuning that turns N-best lists into supervision via three curated pools: corrections (reference in the list but not top-1) to teach selection, regularizers (top-1 correct) to discourage unnecessary rewrites, and hard negatives (reference absent) to suppress hallucination. Fine-tuning on this data provides more effective supervision than random subset fine-tuning for learning a hybrid select-or-rewrite policy, with the clearest gains from around 3B parameters on the proprietary dataset but less conclusive results on CommonVoice, a divergence suggesting that public read-speech text may overlap with LLM pretraining and mask genuine post-editing gains.

Automatic speech recognition (ASR) has improved substantially in recent years, yet performance remains limited for low-resource languages. Large language models (LLMs) have shown promise for improving ASR through generative error correction (GER), but their effectiveness in low-resource settings remains underexplored. In addition, it remains unclear to what extent data contamination influences the reported improvements in LLM-based GER. This study investigates LLM-based GER for low-resource Frisian. In addition to a public corpus, we construct and use a Frisian offline dataset with non-public texts for evaluation to control for potential data contamination. Results show that GER improves ASR performance in most settings, with the best GPT-5.1 results surpassing oracle WERs. Comparable gains on the offline dataset indicate that improvements reflect true correction ability. We further provide a detailed error analysis revealing model correction patterns.

Correct transcription does not establish that a word depended on its local audio. We introduce the Acoustic Evidence Contribution (AEC), a word-level counterfactual measure of acoustic grounding. It is obtained from the change in reference-token log likelihood when the word's aligned acoustic span is replaced. We combine decoding outcomes on unedited audio with thresholded AEC in an outcome-contribution matrix, where correct targets are categorized according to their local acoustic contribution as high or low. Depending on the system, 33-49% of correct single-word targets fall below the twofold contribution threshold when averaged across corpora, despite no evident rank association with WER. Retention of reference words after span removal decreases monotonically across AEC quintiles, from 76-83% in the lowest quintile to approximately 1% in the highest. AEC therefore separates transcription correctness from dependence on the aligned local acoustic span while remaining an oracle-aligned diagnostic method.