标题： 倾听、想象与优化：一种基于大语言模型的启发式自动语音识别纠正框架 显示英文标题

标题： Listening, Imagining & Refining: A Heuristic Optimized ASR Correction Framework with LLMs

摘要： 自动语音识别（ASR）系统仍然容易出现影响下游应用的错误。 在本文中，我们提出LIR-ASR，一种利用大语言模型的启发式优化迭代修正框架，灵感来自人类听觉感知。 LIR-ASR采用“聆听-想象-优化”策略，在上下文中生成语音变体并进行优化。 引入了一种带有有限状态机（FSM）的启发式优化，以防止修正过程陷入局部最优，并且基于规则的约束有助于保持语义一致性。 在英语和中文ASR输出上的实验表明，与基线相比，LIR-ASR在CER/WER上平均降低了多达1.5个百分点，证明了在转录准确性上的显著提升。 显示英文摘要

摘要： Automatic Speech Recognition (ASR) systems remain prone to errors that affect downstream applications. In this paper, we propose LIR-ASR, a heuristic optimized iterative correction framework using LLMs, inspired by human auditory perception. LIR-ASR applies a "Listening-Imagining-Refining" strategy, generating phonetic variants and refining them in context. A heuristic optimization with finite state machine (FSM) is introduced to prevent the correction process from being trapped in local optima and rule-based constraints help maintain semantic fidelity. Experiments on both English and Chinese ASR outputs show that LIR-ASR achieves average reductions in CER/WER of up to 1.5 percentage points compared to baselines, demonstrating substantial accuracy gains in transcription.