Speech-driven visual speech synthesis involves mapping features extracted from acoustic speech to the corresponding lip animation controls for a face model. This mapping can take many forms, but a powerful approach is to use deep neural networks (DNNs). However, a limitation is the lack of synchronized audio, video, and depth data required to reliably train the DNNs, especially for speaker-independent models. In this paper, we investigate adapting an automatic speech recognition (ASR) acoustic model (AM) for the visual speech synthesis problem. We train the AM on ten thousand hours of audio-only data. The AM is then adapted to the visual speech synthesis domain using ninety hours of synchronized audio-visual speech. Using a subjective assessment test, we compared the performance of the AM-initialized DNN to one with a random initialization. The results show that viewers significantly prefer animations generated from the AM-initialized DNN than the ones generated using the randomly initialized model. We conclude that visual speech synthesis can significantly benefit from the powerful representation of speech in the ASR acoustic models.
This paper presents an articulatory modelling approach to convert acoustic speech into realistic mouth animation. We directly model the movements of articulators, such as lips, tongue, and teeth, using a dynamic Bayesian network (DBN)-based audio-visual articulatory model (AVAM). A multiple-stream structure with a shared articulator layer is adopted in the model to synchronously associate the two building blocks of speech, i.e., audio and video. This model not only describes the synchronization between visual articulatory movements and audio speech, but also reflects the linguistic fact that different articulators evolve asynchronously. We also present a Baum-Welch DBN inversion (DBNI) algorithm to generate optimal facial parameters from audio given the trained AVAM under maximum likelihood (ML) criterion. Extensive objective and subjective evaluations on the JEWEL audio-visual dataset demonstrate that compared with phonemic HMM approaches, facial parameters estimated by our approach follow the true parameters more accurately, and the synthesized facial animation sequences are so lively that 38 of them are undistinguishable