  This paper describes a system developed for the GENEA (Generation and
Evaluation of Non-verbal Behaviour for Embodied Agents) Challenge 2023. Our
solution builds on an existing diffusion-based motion synthesis model. We
propose a contrastive speech and motion pretraining (CSMP) module, which learns
a joint embedding for speech and gesture with the aim to learn a semantic
coupling between these modalities. The output of the CSMP module is used as a
conditioning signal in the diffusion-based gesture synthesis model in order to
achieve semantically-aware co-speech gesture generation. Our entry achieved
highest human-likeness and highest speech appropriateness rating among the
submitted entries. This indicates that our system is a promising approach to
achieve human-like co-speech gestures in agents that carry semantic meaning.
