On the Importance of Representations for Speech-Driven Gesture Generation

Abstract

This paper presents a novel framework for automatic speech-driven gesture generation applicable to human-agent interaction, including both virtual agents and robots. Specifically, we extend recent deep-learning-based, data-driven methods for speech-driven gesture generation by incorporating representation learning. Our model takes speech features as input and produces gestures in the form of sequences of 3D joint coordinates representing motion as output. The results of objective and subjective evaluations confirm the benefits of the representation learning.