Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian
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Computer Science > Computation and Language
Title:Can Large Language Models Reliably Correct Errors in Low-Resource ASR? A Contamination-Aware Case Study on West Frisian
Abstract: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.
| Comments: | Submitted to Interspeech 2026 |
| Subjects: | Computation and Language (cs.CL) |
| Cite as: | arXiv:2605.19711 [cs.CL] |
| (or arXiv:2605.19711v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.19711
arXiv-issued DOI via DataCite (pending registration)
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