Automatic 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 **U-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.

In this work, we address the challenge of cross-lingual proper noun recognition in automatic speech recognition (ASR), where proper nouns in an utterance may originate from a language different from the language in which the ASR system is trained. We enhance the performance of end-to-end ASR systems by instructing a large language model (LLM) to correct the ASR model’s predictions. The LLM’s context is augmented with a dictionary of cross-lingual words that are phonetically and graphemically similar to the potentially incorrect proper nouns in the ASR predictions. Our dictionary-based method DiP-ASR (Dictionary-based Prompting for Automatic Speech Recognition) significantly reduces word error rates compared to both the end-to-end ASR baseline and instruction-based prompting of the LLM without the dictionary across cross-lingual proper noun recognition tasks involving three secondary languages.