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A Compact Representation of Visual Speech Data Using Latent Variables

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4 Author(s)
Ziheng Zhou ; Dept. of Comput. Sci. & Eng., Univ. of Oulu, Oulu, Finland ; Xiaopeng Hong ; Guoying Zhao ; Pietikainen, M.

The problem of visual speech recognition involves the decoding of the video dynamics of a talking mouth in a high-dimensional visual space. In this paper, we propose a generative latent variable model to provide a compact representation of visual speech data. The model uses latent variables to separately represent the inter-speaker variations of visual appearances and those caused by uttering, and incorporates the structural information of the observed visual data within an utterance through modelling the structure using a path graph and placing variables' priors along its embedded curve.

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Pattern Analysis and Machine Intelligence, IEEE Transactions on  (Volume:36 ,  Issue: 1 )