标题： 利用大型语言模型进行视觉语音识别：模型扩展、上下文感知解码和迭代优化 显示英文标题

标题： Leveraging Large Language Models in Visual Speech Recognition: Model Scaling, Context-Aware Decoding, and Iterative Polishing

摘要： 视觉语音识别（VSR）通过分析唇部运动来转录语音。最近，大型语言模型（LLMs）已被整合到VSR系统中，带来了显著的性能提升。然而，LLMs的潜力尚未得到充分研究，并且如何在VSR任务中有效利用LLMs仍处于未探索状态。本文系统地探讨了如何更好地利用LLMs进行VSR任务，并提供了三个关键贡献：（1）规模测试：我们研究了LLM规模对VSR性能的影响，确认了VSR任务中的规模定律。（2）上下文感知解码：我们增加了上下文文本以指导LLM解码，提高了识别准确性。（3）迭代优化：我们提出了迭代精炼LLM输出的方法，逐步减少识别错误。广泛的实验表明，通过这些设计，LLMs的巨大潜力可以被充分利用，从而带来显著的VSR性能提升。 显示英文摘要

摘要： Visual Speech Recognition (VSR) transcribes speech by analyzing lip movements. Recently, Large Language Models (LLMs) have been integrated into VSR systems, leading to notable performance improvements. However, the potential of LLMs has not been extensively studied, and how to effectively utilize LLMs in VSR tasks remains unexplored. This paper systematically explores how to better leverage LLMs for VSR tasks and provides three key contributions: (1) Scaling Test: We study how the LLM size affects VSR performance, confirming a scaling law in the VSR task. (2) Context-Aware Decoding: We add contextual text to guide the LLM decoding, improving recognition accuracy. (3) Iterative Polishing: We propose iteratively refining LLM outputs, progressively reducing recognition errors. Extensive experiments demonstrate that by these designs, the great potential of LLMs can be largely harnessed, leading to significant VSR performance improvement.