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arXiv · 2504.05803

PASE: Phoneme-Aware Speech Encoder to Improve Lip Sync Accuracy for Talking Head Synthesis

Yihuan Huang·Jiajun Liu·Yanzhen Ren·Jun Xue·Wuyang Liu·Zongkun Sun
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Abstract

Recent talking head synthesis works typically adopt speech features extracted from large-scale pre-trained acoustic models. However, the intrinsic many-to-many relationship between speech and lip motion causes phoneme-viseme alignment ambiguity, leading to inaccurate and unstable lips. To further improve lip sync accuracy, we propose PASE (Phoneme-Aware Speech Encoder), a novel speech representation model that bridges the gap between phonemes and visemes. PASE explicitly introduces phoneme embeddings as alignment anchors and employs a contrastive alignment module to enhance the discriminability between corresponding audio-visual pairs. In addition, a prediction and reconstruction task is designed to improve robustness under noise and partial modality absence. Experimental results show PASE significantly improves lip sync accuracy and achieves state-of-the-art performance across both NeRF- and 3DGS-based rendering frameworks, outperforming conventional methods based on acoustic features by 13.7 % and 14.2 %, respectively. Importantly, PASE can be seamlessly integrated into diverse talking head pipelines to improve the lip sync accuracy without architectural modifications.

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Yihuan Huang, Jiajun Liu, Yanzhen Ren, Jun Xue, Wuyang Liu, Zongkun Sun. 2025-04-08. PASE: Phoneme-Aware Speech Encoder to Improve Lip Sync Accuracy for Talking Head Synthesis. https://arxiv.org/abs/2504.05803

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