Using IHMM's in audio-to-visual conversion

Abstract

One emerging application which exploits the correlation between audio and video is speech-driven facial animation. The goal of speech-driven facial animation is to synthesize realistic video sequences from acoustic speech. Much of the previous research has implemented this audio-to-visual conversion strategy with existing techniques such as vector quantization and neural networks. In this paper, we examine how this conversion process can be accomplished with hidden Markov models.