Abstract
Generating natural and expressive co-speech gestures for conversational virtual agents and social robots is crucial for enhancing their acceptability and usability in real-world contexts. However, this task is complicated by strong cultural and linguistic influences on gesture patterns, exacerbated by the limited availability of cross-cultural co-speech gesture datasets. To address this gap, we introduce the TED-Culture Dataset, a novel dataset derived from TED talks, designed to enable cross-cultural gesture generation based on linguistic cues. We propose a generative model based on the Stable Diffusion architecture, which we evaluate on both the TED-Expressive Dataset and the TED-Culture Dataset. The model is further implemented on the NAO robot to assess real-time performance. Our model surpasses state-of-the-art baselines in gesture naturalness and exhibits rapid convergence across languages, specifically Indonesian, Japanese, and Italian. Objective and subjective evaluations confirm improvements in communicative effectiveness. Notably, results reveal that individuals are more critical of gestures in their native language, expecting higher generative performance in familiar linguistic contexts. By releasing the TED-Culture Dataset, we facilitate future research on multilingual gesture generation for embodied agents. The study underscores the importance of cultural and linguistic adaptation in co-speech gesture synthesis, with implications for human-robot interaction design.