A compact representation of visual speech data using latent variables.
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.
University of Oulu, Oulu.
Ziheng
Z
Zhou
Xiaopeng
X
Hong
Guoying
G
Zhao
Matti
M
Pietikäinen
D016208:Databases, Factual; D006801:Humans; D010363:Pattern Recognition, Automated / Q000379:methods*; D013060:Speech / Q000502:physiology*; D049250:Speech Recognition Software; D014741:Video Recording / Q000379:methods*