Abstract
Human communication seamlessly integrates speech and bodily motion, where hand gestures naturally complement vocal prosody to express intent, emotion, and emphasis. While recent text-to-speech (TTS) systems have begun incorporating multimodal cues such as facial expressions or lip movements, the role of hand gestures in shaping prosody remains largely underexplored. We propose a novel multimodal TTS framework, Gesture2Speech, that leverages visual gesture cues to modulate prosody in synthesized speech. Motivated by the observation that confident and expressive speakers coordinate gestures with vocal prosody, we introduce a multimodal Mixture-of-Experts (MoE) architecture that dynamically fuses linguistic content and gesture features within a dedicated style extraction module. The fused representation conditions an LLM-based speech decoder, enabling prosodic modulation that is temporally aligned with hand movements. We further design a gesture-speech alignment loss that explicitly models their temporal correspondence to ensure fine-grained synchrony between gestures and prosodic contours. Evaluations on the PATS dataset show that Gesture2Speech outperforms state-of-the-art baselines in both speech naturalness and gesture-speech synchrony. To the best of our knowledge, this is the first work to utilize hand gesture cues for prosody control in neural speech synthesis. Demo samples are available at this https URL
Abstract (translated)
人类交流无缝融合了言语与肢体动作，其中手势自然地与语音韵律相辅相成，以表达意图、情感和强调。尽管近期文本转语音（TTS）系统已开始整合面部表情或唇部动作等多模态线索，但手势在塑造韵律方面的作用仍 largely 未被探索。我们提出了一种新颖的多模态 TTS 框架 Gesture2Speech，该框架利用视觉手势线索来调节合成语音的韵律。受自信且富有表现力的演讲者会协调手势与语音韵律这一观察的启发，我们引入了一种多模态混合专家（MoE）架构，可在专用的风格提取模块内动态融合语言内容与手势特征。融合后的表征条件着一个基于大语言模型的语音解码器，从而实现与手部运动时间对齐的韵律调节。我们还设计了一种手势-语音对齐损失，显式建模它们的时间对应关系，以确保手势与韵律轮廓之间的细粒度同步。在 PATS 数据集上的评估表明，Gestuer2Speech 在语音自然度和手势-语音同步性两方面均优于最新基线方法。据我们所知，这是首个利用手部手势线索进行神经语音合成中韵律控制的工作。演示样本可在此 https URL 获取。
URL
https://arxiv.org/abs/2603.19831
