Post-ASR Correction in Hindi: Comparing Language Models and Large Language Models in Low-Resource Scenarios

AbstractAutomatic Speech Recognition (ASR) systems for low-resource languages like Hindi often produce erroneous transcripts due to limited annotated data and linguistic complexity. Post-ASR correction using language models (LMs) and large language models (LLMs) offers a promising approach to improve transcription quality. In this work, we compare fine-tuned LMs (mT5, ByT5), fine-tuned LLMs (Nanda 10B), and instruction-tuned LLMs (GPT-4o-mini, LLaMA variants) for post-ASR correction in Hindi. Our findings reveal that smaller, fine-tuned models consistently outperform larger LLMs in both fine-tuning and in-context learning (ICL) settings. We observe a n-shaped inverse scaling trend under zero-shot ICL, where mid-sized LLMs degrade performance before marginal recovery at extreme scales, yet still fall short of fine-tuned models. ByT5 is more effective for character-level corrections such as transliteration and word segmentation, while mT5 handles broader semantic inconsistencies. We also identify performance drops in out-of-domain settings and propose mitigation strategies to preserve domain fidelity. In particular, we observe similar trends in Marathi and Telugu, indicating the broader applicability of our findings across low-resource Indian languages.