A compact representation of visual speech data using latent variables
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Updated: May 6, 2026

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
Published on: August 9, 2024
Ziheng Zhou1, Xiaopeng Hong, Guoying Zhao
1University of Oulu, Oulu.
Abstract:
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 interspeaker variations of visual appearances and those caused by uttering within images, and incorporates the structural information of the visual data through placing priors of the latent variables along a curve embedded within a path graph.
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