  Due to their significance in human communication, the automatic generation of
co-speech gestures in artificial embodied agents has received a lot of
attention. Although modern deep learning approaches can generate
realistic-looking conversational gestures from spoken language, they often lack
the ability to convey meaningful information and generate contextually
appropriate gestures. This paper presents an augmented approach to the
generation of co-speech gestures that additionally takes into account given
form and meaning features for the gestures. Our framework effectively acquires
this information from a small corpus with rich semantic annotations and a
larger corpus without such information. We provide an analysis of the effects
of distinctive feature targets and we report on a human rater evaluation study
demonstrating that our framework achieves semantic coherence and person
perception on the same level as human ground truth behavior. We make our data
pipeline and the generation framework publicly available.
