Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and
  Context-Aware Visual Speech Processing
Jeong Hun Yeo Seunghee Han Minsu Kim Yong Man Ro
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 a 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 Adaptors (LoRA), VSP-LLM can be trained in a computationally efficient
manner. In the translation dataset, the MuAViC benchmark, we demonstrate that
VSP-LLM can more effectively recognize and translate lip movements with just 15
hours of labeled data, compared to the recent translation model trained with
433 hours of labeld data.