Phoneme-Level Visual Speech Recognition via Point-Visual Fusion and Language Model Reconstruction

• URL: http://arxiv.org/abs/2507.18863v1
• Date: Fri, 25 Jul 2025 00:38:39 GMT
• Title: Phoneme-Level Visual Speech Recognition via Point-Visual Fusion and Language Model Reconstruction
• Authors: Matthew Kit Khinn Teng, Haibo Zhang, Takeshi Saitoh,
• Abstract summary: Visual Automatic Speech Recognition (V-ASR) is a challenging task that involves interpreting spoken language solely from visual information, such as lip movements and facial expressions.<n>Existing methods often aim to predict words directly from visual cues, but they commonly suffer from high error rates due to viseme ambiguity.<n>We propose a novel phoneme-based two-stage framework that fuses visual and landmark motion features.
• Score: 1.778037147204838
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual Automatic Speech Recognition (V-ASR) is a challenging task that involves interpreting spoken language solely from visual information, such as lip movements and facial expressions. This task is notably challenging due to the absence of auditory cues and the visual ambiguity of phonemes that exhibit similar visemes-distinct sounds that appear identical in lip motions. Existing methods often aim to predict words or characters directly from visual cues, but they commonly suffer from high error rates due to viseme ambiguity and require large amounts of pre-training data. We propose a novel phoneme-based two-stage framework that fuses visual and landmark motion features, followed by an LLM model for word reconstruction to address these challenges. Stage 1 consists of V-ASR, which outputs the predicted phonemes, thereby reducing training complexity. Meanwhile, the facial landmark features address speaker-specific facial characteristics. Stage 2 comprises an encoder-decoder LLM model, NLLB, that reconstructs the output phonemes back to words. Besides using a large visual dataset for deep learning fine-tuning, our PV-ASR method demonstrates superior performance by achieving 17.4% WER on the LRS2 and 21.0% WER on the LRS3 dataset.

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