  In visual speech processing, context modeling capability is one of the most
important requirements due to the ambiguous nature of lip movements. For
example, homophenes, words that share identical lip movements but produce
different sounds, can be distinguished by considering the context. In this
paper, we propose a novel framework, namely Visual Speech Processing
incorporated with LLMs (VSP-LLM), to maximize the context modeling ability by
bringing the overwhelming power of LLMs. Specifically, VSP-LLM is designed to
perform multi-tasks of visual speech recognition and translation, where the
given instructions control the type of task. The input video is mapped to the
input latent space of an LLM by employing a self-supervised visual speech
model. Focused on the fact that there is redundant information in input frames,
we propose a novel deduplication method that reduces the embedded visual
features by employing visual speech units. Through the proposed deduplication
and Low Rank Adaptation (LoRA), VSP-LLM can be trained in a computationally
efficient manner. In the translation dataset, the MuAViC benchmark, we
demonstrate that VSP-LLM trained on just 30 hours of labeled data can more
effectively translate lip movements compared to the recent model trained with
433 hours of data.
