  Communicative gestures and speech acoustic are tightly linked. Our objective
is to predict the timing of gestures according to the acoustic. That is, we
want to predict when a certain gesture occurs. We develop a model based on a
recurrent neural network with attention mechanism. The model is trained on a
corpus of natural dyadic interaction where the speech acoustic and the gesture
phases and types have been annotated. The input of the model is a sequence of
speech acoustic and the output is a sequence of gesture classes. The classes we
are using for the model output is based on a combination of gesture phases and
gesture types. We use a sequence comparison technique to evaluate the model
performance. We find that the model can predict better certain gesture classes
than others. We also perform ablation studies which reveal that fundamental
frequency is a relevant feature for gesture prediction task. In another
sub-experiment, we find that including eyebrow movements as acting as beat
gesture improves the performance. Besides, we also find that a model trained on
the data of one given speaker also works for the other speaker of the same
conversation. We also perform a subjective experiment to measure how
respondents judge the naturalness, the time consistency, and the semantic
consistency of the generated gesture timing of a virtual agent. Our respondents
rate the output of our model favorably.
