Head motion synthesis from speech using deep neural networks

This paper presents a deep neural network (DNN) approach for head motion synthesis, which can automatically predict head movement of a speaker from his/her speech. Specifically, we realize speech-to-head-motion mapping by learning a DNN from audio-visual broadcast news data. We first show that a generatively pre-trained neural network significantly outperforms a conventional randomly initialized network. We then demonstrate that filter bank (FBank) features outperform mel frequency cepstral coefficients (MFCC) and linear prediction coefficients (LPC) in head motion prediction. Finally, we discover that extra training data from other speakers used in the pre-training stage can improve the head motion prediction performance of a target speaker. Our promising results in speech-to-head-motion prediction can be used in talking avatar animation.