Audio-visual speech modeling using coupled hidden Markov models
Chu, S.M.; Huang, T.S.
Acoustics, Speech, and Signal Processing, 2002. Proceedings. (ICASSP apos;02). IEEE International Conference on
Volume 2, Issue , 2002 Page(s):2009 - 2012
Digital Object Identifier  
Summary:In this work we consider the bimodal fusion problem in audiovisual speech recognition. A novel sensory fusion architecture based on the coupled hidden Markov models (CHMM) is presented. CHMM 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 CHMM. Results from audiovisual speech recognition experiments confirmed the superior capability of the proposed fusion architecture

» View citation and abstract

Log in by entering your IEEE Web Account Username and Password.

IEEE Communications Society members: If you subscribe to the IEEE Electronic Periodicals Package or IEEE Electronic Periodicals Package Plus, you must access your subscription at www.comsoc.org.

Check with your librarian, information professional, or system manager to determine if you need to log in. Please complete the online Technical Support Form if you need assistance.

Select the Purchase History link to access the document. You will have 5 Days after purchase to access the Full Text PDF. Please complete the online Technical Support Form if you need assistance.

• Search and access Abstract records free of charge
• Register for table of contents alerts
• Purchase Full Text PDF documents

» Learn more about subscription options or how to become an IEEE Member.