Abstract
Co-speech gestures can increase engagement and improve speech understanding. Yet, most data-driven robot systems generate either rhythmic beat-like motion or semantically meaningful iconic gestures, but not both. We present an emotion-aware rhythmic-semantic co-speech gesture system that generates iconic and beat behaviors in real time from text and the robot's internal emotional state. We formalize the task of beat-iconic gesture generation using emotion and propose lightweight models that predict iconic timing and intensity, then fill the remaining spans with emotion-conditioned beats at an appropriate intensity level. We train on the BEAT2 dataset and deploy the system on the social robot Haru. In an online user study with 60 participants, our method shows stronger speech-gesture alignment and is preferred over baseline methods.
Abstract (translated)
URL
https://arxiv.org/abs/2611.13472
