Learning dynamic audio-visual mapping with input-output hidden Markov models

In this paper, we formulate the problem of synthesizing facial animation from an input audio sequence as a dynamic audio-visual mapping. We propose that audio-visual mapping should be modeled with an input-output hidden Markov model, or IOHMM. An IOHMM is an HMM for which the output and transition probabilities are conditional on the input sequence. We train IOHMMs using the expectation-maximization (EM) algorithm with a novel architecture to explicitly model the relationship between transition probabilities and the input using neural networks. Given an input sequence, the output sequence is synthesized by the maximum likelihood estimation. Experimental results demonstrate that IOHMMs can generate natural and good-quality facial animation sequences from the input audio.