  In this paper, we investigate the usage of large language models (LLMs) to
improve the performance of competitive speech recognition systems. Different
from previous LLM-based ASR error correction methods, we propose a novel
multi-stage approach that utilizes uncertainty estimation of ASR outputs and
reasoning capability of LLMs. Specifically, the proposed approach has two
stages: the first stage is about ASR uncertainty estimation and exploits N-best
list hypotheses to identify less reliable transcriptions; The second stage
works on these identified transcriptions and performs LLM-based corrections.
This correction task is formulated as a multi-step rule-based LLM reasoning
process, which uses explicitly written rules in prompts to decompose the task
into concrete reasoning steps. Our experimental results demonstrate the
effectiveness of the proposed method by showing 10% ~ 20% relative improvement
in WER over competitive ASR systems -- across multiple test domains and in
zero-shot settings.
