  Although humans engaged in face-to-face conversation simultaneously
communicate both verbally and non-verbally, methods for joint and unified
synthesis of speech audio and co-speech 3D gesture motion from text are a new
and emerging field. These technologies hold great promise for more human-like,
efficient, expressive, and robust synthetic communication, but are currently
held back by the lack of suitably large datasets, as existing methods are
trained on parallel data from all constituent modalities. Inspired by
student-teacher methods, we propose a straightforward solution to the data
shortage, by simply synthesising additional training material. Specifically, we
use unimodal synthesis models trained on large datasets to create multimodal
(but synthetic) parallel training data, and then pre-train a joint synthesis
model on that material. In addition, we propose a new synthesis architecture
that adds better and more controllable prosody modelling to the
state-of-the-art method in the field. Our results confirm that pre-training on
large amounts of synthetic data improves the quality of both the speech and the
motion synthesised by the multimodal model, with the proposed architecture
yielding further benefits when pre-trained on the synthetic data. See
https://shivammehta25.github.io/MAGI/ for example output.
