  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 as input and produces gestures as output, in
the form of a sequence of 3D coordinates. Our approach consists of two steps.
First, we learn a lower-dimensional representation of human motion using a
denoising autoencoder neural network, consisting of a motion encoder MotionE
and a motion decoder MotionD. The learned representation preserves the most
important aspects of the human pose variation while removing less relevant
variation. Second, we train a novel encoder network SpeechE to map from speech
to a corresponding motion representation with reduced dimensionality. At test
time, the speech encoder and the motion decoder networks are combined: SpeechE
predicts motion representations based on a given speech signal and MotionD then
decodes these representations to produce motion sequences. We evaluate
different representation sizes in order to find the most effective
dimensionality for the representation. We also evaluate the effects of using
different speech features as input to the model. We find that mel-frequency
cepstral coefficients (MFCCs), alone or combined with prosodic features,
perform the best. The results of a subsequent user study confirm the benefits
of the representation learning.
