Sequence-to-Sequence Predictive Model: From Prosody To Communicative Gestures

TL;DR

This paper presents a neural network model that predicts the timing of communicative gestures based on speech acoustics, demonstrating its effectiveness and the importance of features like fundamental frequency and eyebrow movements.

Contribution

The study introduces a sequence-to-sequence neural model for gesture timing prediction from speech, including novel findings on feature relevance and cross-speaker applicability.

Findings

The model predicts certain gesture classes more accurately.

Fundamental frequency is a key feature for prediction.

Including eyebrow movements improves performance.

Abstract

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.