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A Statistical Quality Model for Data-Driven Speech Animation.

Xiaohan Ma1, Zhigang Deng

  • 1Computer Graphics Lab, Department of Computer Science, University of Houston, PGH 309, 4800 Calhoun Road, Houston, TX 77204-3010, USA. xiaohan@cs.uh.edu

IEEE Transactions on Visualization and Computer Graphics
|February 22, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model (SAQP) to automatically assess the quality of synthesized speech animations. This innovation addresses the challenge of evaluating realism in data-driven animation, paving the way for better talking avatars.

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Area of Science:

  • Computer Graphics
  • Artificial Intelligence
  • Human-Computer Interaction

Background:

  • Data-driven speech animation techniques have advanced significantly, improving animation quality.
  • Automatic evaluation of synthesized speech animation realism remains a critical unsolved problem.

Purpose of the Study:

  • To propose a novel statistical model (SAQP) for automatically predicting the quality of synthesized speech animations.
  • To establish a quantitative quality assessment for data-driven speech animation.

Main Methods:

  • Developed a phoneme-based Speech Animation Trajectory Fitting (SATF) metric to quantify synthesis errors.
  • Constructed a statistical regression model linking the SATF metric to objective animation quality.
  • Validated the SAQP model through user studies.

Main Results:

  • The SAQP model effectively predicts the quality of synthesized speech animations.
  • The SATF metric accurately describes speech animation synthesis errors.
  • User studies confirmed the model's effectiveness and robustness.

Conclusions:

  • The SAQP model represents a pioneering quantitative quality assessment for data-driven speech animation.
  • This work is a crucial step towards enabling real-time applications of talking avatars.
  • The proposed method facilitates the integration of advanced speech animation into interactive systems.