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Where Visual Speech Meets Language: VSP-LLM Framework for Efficient and Context-Aware Visual Speech Processing (2402.15151v2)

Published 23 Feb 2024 in cs.CV, cs.CL, eess.AS, and eess.IV

Abstract: 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.

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Citations (6)
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Summary

  • The paper presents a novel integration of visual speech processing with large language models to enhance context modeling and resolve homophenes.
  • It introduces a unified model for visual speech recognition and translation while employing an innovative deduplication strategy for improved efficiency.
  • The framework outperforms existing models using minimal labeled data, demonstrating robust performance in low-resource scenarios.

Enhancing Visual Speech Processing with LLMs: Introducing the VSP-LLM Framework

Introduction to the VSP-LLM Framework

In the landscape of visual speech processing, the challenge of deciphering homophenes—words with identical lip movements but different sounds—underscores the pivotal role of context modeling. Harnessing the prowess of LLMs, the novel VSP-LLM (Visual Speech Processing incorporated with LLMs) framework emerges as a solution geared towards amplifying context modeling capabilities for visual speech recognition (VSR) and translation (VST). By integrating a self-supervised visual speech model for phoneme-level representation and pioneering deduplication methods to enhance computational efficiency, this framework takes a giant leap forward in the field.

Key Contributions

The paper's key contributions are multi-fold and significantly impact both the theoretical and practical realms of visual speech processing:

  • Pioneering Integration: It stands as the inaugural effort to amalgamate visual speech processing with LLMs, achieving unprecedented performances in VSR and VST tasks.
  • Unified Model: This marks the first time a single model has demonstrated competency in both VSR and VST, showcasing versatility.
  • Novel Deduplication Approach: The introduction of a visual speech deduplication strategy markedly bolsters computational efficiency.
  • Demonstrated Efficacy with Limited Data: Impressively, the VSP-LLM framework outperforms existing models with a fraction of the labeled data, demonstrating its robustness and efficiency.

Theoretical and Practical Implications

The fusion of visual speech processing with LLMs opens a new avenue for exploring the rich context modeling capability of LLMs to mitigate the ambiguity innate to homophenes. By effectively embedding visual speech into the linguistic space, the VSP-LLM framework not only sharpens the accuracy of speech recognition and translation but also lays the groundwork for future exploration in multimodal AI applications. Practically, the ability to perform with limited resources underscores the framework's potential in low-resource scenarios, enhancing accessibility and applicability in diverse environments.

Speculations on Future Developments

The integration of visual speech processing with LLMs heralds a promising trajectory toward creating more nuanced and context-aware AI systems. Future developments may pivot on enhancing the granularity of phoneme-level representations and expanding the framework's applicability across a wider array of languages and dialects. Furthermore, investigating the scalability of the deduplication method and its adaptability to other forms of sequential data presents an intriguing research direction. The VSP-LLM framework, with its pioneering accomplishments, sets a solid foundation for advancing the synergy between visual speech and language processing realms.

Conclusion

In essence, the VSP-LLM framework signifies a significant stride forward in the convergence of visual speech and language processing. Through its innovative integration of LLMs, it achieves not only superior performance in tasks of VSR and VST but also manifests a remarkable efficiency in computational resource usage. This exploration opens up new possibilities for research and application, setting a benchmark for future endeavors in the domain of visual speech processing and beyond.

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