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A compact representation of visual speech data using latent variables.

Ziheng Zhou1, Xiaopeng Hong, Guoying Zhao

  • 1University of Oulu, Oulu.

IEEE Transactions on Pattern Analysis and Machine Intelligence
|November 16, 2013
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Summary
This summary is machine-generated.

This study introduces a new generative model for visual speech recognition. It compactly represents talking mouth movements by separating speaker and utterance variations for better decoding.

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Area of Science:

  • Computer Vision
  • Machine Learning
  • Speech Processing

Background:

  • Visual speech recognition decodes talking mouth movements in high-dimensional visual spaces.
  • Existing methods face challenges in compactly representing complex visual speech data.

Purpose of the Study:

  • To propose a generative latent variable model for compact visual speech data representation.
  • To effectively model inter-speaker variations and utterance-specific dynamics.

Main Methods:

  • Developed a generative latent variable model.
  • Utilized latent variables to distinguish between visual appearance variations (inter-speaker) and utterance dynamics.
  • Incorporated structural information via priors on latent variables along a path graph.

Main Results:

  • The proposed model offers a compact representation of visual speech data.
  • Demonstrated effective separation of inter-speaker variations and utterance-specific visual dynamics.
  • Integrated structural information for enhanced data representation.

Conclusions:

  • The generative latent variable model provides an effective approach for compact visual speech data representation.
  • This method enhances visual speech recognition by modeling key variations distinctly.
  • The approach shows promise for improving the decoding of visual speech dynamics.