Multimodal speech driven facial shape animation using deep neural networks

Abstract

In this paper we present a deep learning multimodal approach for speech driven generation of face animations. Training a speaker independent model, capable of generating different emotions of the speaker, is crucial for realistic animations. Unlike the previous approaches which either use acoustic features or phoneme label features to estimate the facial movements, we utilize both modalities to generate natural looking speaker independent lip animations synchronized with affective speech. A phoneme-based model qualifies generation of speaker independent animation, whereas an acoustic feature-based model enables capturing affective variation during the animation generation. We show that our multimodal approach not only performs significantly better on affective data, but improves performance over neutral data as well. We evaluate the proposed multimodal speech-driven animation model using two large scale datasets, GRID and SAVEE, by reporting the mean squared error (MSE) over various network structures.