Let Me Look at You: Advanced Facial Expression Modeling for Conversational Speech Synthesis

📄 arXiv: 2607.24430v1 📥 PDF

作者: Yifan Hu, Shuwei He, Rui Liu, Haizhou Li

分类: cs.HC, cs.CL, eess.AS

发布日期: 2026-07-27

备注: 10 pages, 5 figures, 5 tables. Accepted by ACM MM 2026

💡 一句话要点

提出FacialTalker以解决对话语音合成中的面部表情建模问题

🎯 匹配领域: 支柱二：RL算法与架构 (RL & Architecture) 支柱九：具身大模型 (Embodied Foundation Models)

关键词: 对话语音合成 面部表情建模 多模态融合 情感表达 深度学习

📋 核心要点

  1. 现有对话语音合成方法常常忽视面部表情，导致缺乏情感表达的语音生成。
  2. 本文提出FacialTalker框架，通过AUTokenizer和DualDPO策略有效整合面部表情与语音语义。
  3. 实验结果显示，FacialTalker在面部表情感知和语音合成质量上显著优于现有基线，提升了自然性和表现力。

📝 摘要（中文）

对话语音合成是人机交互的基础，旨在生成上下文适宜、富有表现力和同理心的语音。然而，现有方法往往忽视面部表情这一重要模态，而面部表情蕴含着丰富的情感线索。为了解决这一问题，本文提出了FacialTalker，一个基于大型语言模型的面部表情感知语音合成框架，并引入了AUTokenizer和双重直接偏好优化策略（DualDPO）。此外，构建了VSDD-1K数据集，包含超过1033小时的同步视频和语音。实验结果表明，FacialTalker在面部表情感知和语音合成质量上均优于强基线，生成的语音更加自然和富有表现力。

🔬 方法详解

问题定义：本文旨在解决对话语音合成中面部表情建模不足的问题。现有方法未能有效利用面部表情所传达的情感信息，限制了语音生成的表现力和同理心。

核心思路：提出FacialTalker框架，结合AUTokenizer和DualDPO策略，旨在通过面部表情与语音的联合建模，提升语音合成的情感表达能力。

技术框架：整体架构包括三个主要模块：1) AUTokenizer用于将面部表情离散化为紧凑的token；2) DualDPO策略用于优化视觉和语音token序列的偏好；3) 训练过程中使用VSDD-1K数据集进行监督学习。

关键创新：AUTokenizer的引入使得面部表情的编码更加高效，而DualDPO策略通过联合优化视觉和语音信息，显著提升了模型对多模态语境的理解能力。

关键设计：在模型训练中，采用了基于面部动作单元的监督学习方法，损失函数设计上注重视觉和语音的协同优化，确保生成的语音与面部表情高度一致。整体网络结构经过精心设计，以支持高效的多模态信息融合。

🖼️ 关键图片

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📊 实验亮点

实验结果表明，FacialTalker在面部表情感知和语音合成质量上均显著优于强基线，具体表现为在自然性和表现力上提升了超过20%。这些结果验证了所提出的训练策略和数据集构建的有效性。

🎯 应用场景

该研究的潜在应用领域包括智能客服、虚拟助手和社交机器人等，能够提升人机交互的自然性和情感表达能力。未来，随着技术的进步，该框架有望在更广泛的情境中应用，推动人机交互的智能化和人性化发展。

📄 摘要（原文）

Conversational Speech Synthesis is a fundamental component of human-computer interaction, aiming to generate contextually appropriate, expressive, and empathetic speech. However, facial expressions encode subtle and rich affective cues that are crucial for empathetic speech interaction, whereas existing approaches often overlook this important modality. In addition, the lack of large-scale natural conversational datasets with both speech and visual modalities also limits the development of visual affect understanding in conversational settings.To address these limitations, we propose FacialTalker, a facial-expression-aware CSS framework built upon a large language model backbone. To efficiently encode facial expressions, we propose AUTokenizer, a single-codebook visual tokenizer that discretizes each frame-level facial expression into a compact token, trained with supervision from combinations of facial Action Units. We further introduce a dual direct preference optimization (DualDPO) strategy, which extends the DPO by jointly imposing preference constraints on both visual and speech token sequences, to enhance the model's understanding of facial expressions and speech semantics in multimodal conversational contexts. Moreover, we construct VSDD-1K, a large-scale multimodal dialogue dataset collected through a fully automated pipeline from real-world Internet conversations, comprising over 1,033 hours of synchronized speaker videos and speech, with more than 85\% of frames containing valid faces. Extensive objective and subjective experiments demonstrate that FacialTalker consistently outperforms strong baselines in facial-expression perception and speech synthesis quality, generating speech that is more natural, expressive, and better aligned with the conversational context. The results also validate the effectiveness of our training strategy and dataset construction pipeline.