Audio-visual speech modeling using coupled hidden Markov models
In this work we consider the bimodal fusion problem in audio-visual speech recognition. A novel sensory fusion architecture based on the coupled hidden Markov models (CHMMs) is presented. CHMMs are directed graphical models of stochastic processes and are a special type of dynamic Bayesian networks. The proposed fusion architecture allows us to address the statistical modeling and the fusion of audio-visual speech in a unified framework. Furthermore, the architecture is capable of capturing the asynchronous and temporal inter-modal dependencies between the two information channels. We describe a model transformation strategy to facilitate inference and learning in CHMMs. Results from audio-visual speech recognition experiments confirmed the superior capability of the proposed fusion architecture.