Accurate visible speech synthesis based on concatenating variable length motion capture data

IEEE Transactions on Visualization and Computer Graphics
Jiyong MaBarbara Wise

Abstract

We present a novel approach to synthesizing accurate visible speech based on searching and concatenating optimal variable-length units in a large corpus of motion capture data. Based on a set of visual prototypes selected on a source face and a corresponding set designated for a target face, we propose a machine learning technique to automatically map the facial motions observed on the source face to the target face. In order to model the long distance coarticulation effects in visible speech, a large-scale corpus that covers the most common syllables in English was collected, annotated and analyzed. For any input text, a search algorithm to locate the optimal sequences of concatenated units for synthesis is desrcribed. A new algorithm to adapt lip motions from a generic 3D face model to a specific 3D face model is also proposed. A complete, end-to-end visible speech animation system is implemented based on the approach. This system is currently used in more than 60 kindergarten through third grade classrooms to teach students to read using a lifelike conversational animated agent. To evaluate the quality of the visible speech produced by the animation system, both subjective evaluation and objective evaluation are conducted. T...Continue Reading

References

Nov 19, 2003·Advanced Drug Delivery Reviews·M GeigerW Friess

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Citations

Feb 22, 2012·IEEE Transactions on Visualization and Computer Graphics·Xiaohan Ma, Zhigang Deng
Sep 29, 2018·International Journal of Audiology·Annelies DevesseJan Wouters

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