Imagine a world where machines not only transcribe your voice but also correct their own mistakes. That's the exciting potential of integrating Large Language Models (LLMs) with Automatic Speech Recognition (ASR) systems. Researchers are exploring ways to use LLMs like ChatGPT to polish ASR transcripts, essentially giving them a second chance to get it right. One of the big challenges is avoiding the introduction of new errors while fixing existing ones. Think of it like auto-correct, sometimes helpful, sometimes disastrous. Scientists are tackling this with "confidence-based filtering." They're teaching the LLM to focus on parts of the transcript where the ASR system is less certain, minimizing the risk of unnecessary changes. Early results show promising improvements, especially with smaller, less powerful ASR models. The research focused on the LibriSpeech dataset, a collection of audiobook recordings. Using different sizes of Whisper ASR models and varying LLM prompts, researchers found that strategic prompting plays a key role in success. For instance, instructing the LLM to prioritize phonetically similar corrections significantly boosted performance. The team also investigated the impact of ASR and LLM model sizes. Interestingly, less accurate ASR systems saw bigger improvements, indicating that LLMs can be particularly valuable in areas where current speech technology isn't perfect. Though results vary depending on both the initial ASR accuracy and the chosen LLM, this research highlights the potential for LLMs to act as a powerful cleanup crew for speech recognition. Further research will explore new approaches to confidence estimation, investigate performance across different datasets and languages, and examine whether re-scoring LLM output with the acoustic model can further boost accuracy. This work hints at a future where AI can correct itself, leading to more reliable and user-friendly speech technology.