A Statistical Quality Model for Data-Driven Speech Animation

IEEE Transactions on Visualization and Computer Graphics
Xiaohan Ma, Zhigang Deng

Abstract

In recent years, data-driven speech animation approaches have achieved significant successes in terms of animation quality. However, how to automatically evaluate the realism of novel synthesized speech animations has been an important yet unsolved research problem. In this paper, we propose a novel statistical model (called SAQP) to automatically predict the quality of on-the-fly synthesized speech animations by various data-driven techniques. Its essential idea is to construct a phoneme-based, Speech Animation Trajectory Fitting (SATF) metric to describe speech animation synthesis errors and then build a statistical regression model to learn the association between the obtained SATF metric and the objective speech animation synthesis quality. Through delicately designed user studies, we evaluate the effectiveness and robustness of the proposed SAQP model. To the best of our knowledge, this work is the first-of-its-kind, quantitative quality model for data-driven speech animation. We believe it is the important first step to remove a critical technical barrier for applying data-driven speech animation techniques to numerous online or interactive talking avatar applications.

References

May 5, 2005·IEEE Transactions on Visualization and Computer Graphics·Scott A King, Richard E Parent
Mar 3, 2006·IEEE Transactions on Visualization and Computer Graphics·Jiyong MaBarbara Wise
Nov 13, 2007·IEEE Transactions on Visualization and Computer Graphics·Mario Botsch, Olga Sorkine
Nov 14, 2008·IEEE Computer Graphics and Applications·Qing Li, Zhigang Deng

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