查看文章

This paper presents an investigation into predicting the movement of a speaker’s mouth from text input using hidden Markov models (HMM). A corpus of human articulatory movements, recorded by electromagnetic articulography (EMA), is used to train HMMs. To predict articulatory movements for input text, a suitable model sequence is selected and a maximum-likelihood parameter generation (MLPG) algorithm is used to generate output articulatory trajectories. Unified acoustic-articulatory HMMs are introduced to integrate acoustic features when an acoustic signal is also provided with the input text. Several aspects of this method are analyzed in this paper, including the effectiveness of context-dependent modeling, the role of supplementary acoustic input, and the appropriateness of certain model structures for the unified acoustic-articulatory models. When text is the sole input, we find that fully context-dependent …