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October 15, 2025Open Access

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

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Analysis reveals how large language models improve recognition accuracy in visual speech recognition, suggesting innovative approaches.

Integrating large language models significantly enhances visual speech recognition performance, applying scaling laws.
Key findings include improved recognition accuracy through context-aware decoding and iterative polishing techniques.
Experimental results confirm the effectiveness of LLM scaling, adding context to the decoding process for improved outcomes.
These insights highlight innovative methods to maximize the potential of large language models in visual speech recognition.

Liu et al. (2025) studied this question.

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October 15, 2025Open Access

View Full Paper

Ask AI

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Authors

Analysis reveals how large language models improve recognition accuracy in visual speech recognition, suggesting innovative approaches.

Integrating large language models significantly enhances visual speech recognition performance, applying scaling laws.
Key findings include improved recognition accuracy through context-aware decoding and iterative polishing techniques.
Experimental results confirm the effectiveness of LLM scaling, adding context to the decoding process for improved outcomes.
These insights highlight innovative methods to maximize the potential of large language models in visual speech recognition.

Liu et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark