  Co-speech gestures enhance interaction experiences between humans as well as
between humans and robots. Existing robots use rule-based speech-gesture
association, but this requires human labor and prior knowledge of experts to be
implemented. We present a learning-based co-speech gesture generation that is
learned from 52 h of TED talks. The proposed end-to-end neural network model
consists of an encoder for speech text understanding and a decoder to generate
a sequence of gestures. The model successfully produces various gestures
including iconic, metaphoric, deictic, and beat gestures. In a subjective
evaluation, participants reported that the gestures were human-like and matched
the speech content. We also demonstrate a co-speech gesture with a NAO robot
working in real time.
