Towards Inclusive Communication: A Unified LLM-Based Framework for Sign Language, Lip Movements, and Audio Understanding

• URL: http://arxiv.org/abs/2508.20476v1
• Date: Thu, 28 Aug 2025 06:51:42 GMT
• Title: Towards Inclusive Communication: A Unified LLM-Based Framework for Sign Language, Lip Movements, and Audio Understanding
• Authors: Jeong Hun Yeo, Hyeongseop Rha, Sungjune Park, Junil Won, Yong Man Ro,
• Abstract summary: We introduce the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation.<n>We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or better than state-of-the-art models specialized for individual tasks.
• Score: 52.859261069569165
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Audio is the primary modality for human communication and has driven the success of Automatic Speech Recognition (ASR) technologies. However, such systems remain inherently inaccessible to individuals who are deaf or hard of hearing. Visual alternatives such as sign language and lip reading offer effective substitutes, and recent advances in Sign Language Translation (SLT) and Visual Speech Recognition (VSR) have improved audio-less communication. Yet, these modalities have largely been studied in isolation, and their integration within a unified framework remains underexplored. In this paper, we introduce the first unified framework capable of handling diverse combinations of sign language, lip movements, and audio for spoken-language text generation. We focus on three main objectives: (i) designing a unified, modality-agnostic architecture capable of effectively processing heterogeneous inputs; (ii) exploring the underexamined synergy among modalities, particularly the role of lip movements as non-manual cues in sign language comprehension; and (iii) achieving performance on par with or superior to state-of-the-art models specialized for individual tasks. Building on this framework, we achieve performance on par with or better than task-specific state-of-the-art models across SLT, VSR, ASR, and AVSR. Furthermore, our analysis reveals that explicitly modeling lip movements as a separate modality significantly improves SLT performance.

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