让口型生成的语音更自然，提升语调一致性。
LipSody: Lip-to-Speech Synthesis with Enhanced Prosody Consistency
- 用面部身份、唇动内容和情绪三类线索引导语音语调。
- 相比之前方法，语调偏差降低23%，音量一致性提升18%。
- 适合语音重建、影视修复等需要自然语音的应用场景。
唇动到语音合成旨在通过无声面部视频直接生成语音音频，重建唇部运动中的语言内容，在音频缺失或受损时具有重要应用价值。尽管基于扩散模型的最新方法（如LipVoicer）在语言内容重建上表现优异，但普遍存在语调不一致问题。本文提出LipSody框架，通过引入语调引导策略，融合三种互补线索：从面部图像提取的说话人身份、从唇部运动推断的语言内容，以及从面部视频推断的情绪上下文。实验结果表明，与先前方法相比，LipSody在全局和局部音高偏差、能量一致性及说话人相似性等语调相关指标上均有显著提升。
原文摘要 · Abstract (English)
Lip-to-speech synthesis aims to generate speech audio directly from silent facial video by reconstructing linguistic content from lip movements, providing valuable applications in situations where audio signals are unavailable or degraded. While recent diffusion-based models such as LipVoicer have demonstrated impressive performance in reconstructing linguistic content, they often lack prosodic consistency. In this work, we propose LipSody, a lip-to-speech framework enhanced for prosody consistency. LipSody introduces a prosody-guiding strategy that leverages three complementary cues: speaker identity extracted from facial images, linguistic content derived from lip movements, and emotional context inferred from face video. Experimental results demonstrate that LipSody substantially improves prosody-related metrics, including global and local pitch deviations, energy consistency, and speaker similarity, compared to prior approaches.
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