A realistic 3D articulatory animation system for emotional visual pronunciation

A realistic 3D articulatory animation system for emotional visual pronunciation This paper proposes a realistic 3D articulatory animation system for emotional visual pronunciation driven by the emotional articulatory movement trajectory. Firstly, the articulatory movements, recorded by Electro-Magnetic Articulatory (EMA), are trained by Hidden Markov Model (HMM) while the fully context-dependent model is taken into account by making full use of the rich linguistic features. Secondly, owing to the independency in the manipulation of articulators, the articulatory movements are more remarkably adjusted to express different emotions. Thirdly, the emotional speech is generated by adjusting the neutral speech parameters, such as fundamental frequency (F0), duration and intensity, based on Praat. Then the corresponding articulatory movements are synthesized by the HMM prediction rules which are used to drive the head mesh model along with the emotional speech simultaneously. The experiments intend to synthesize accurate emotional speech synchronized animation of articulators based on the system at phoneme level. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Multimedia Tools and Applications Springer Journals

A realistic 3D articulatory animation system for emotional visual pronunciation

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Publisher
Springer US
Copyright
Copyright © 2017 by Springer Science+Business Media New York
Subject
Computer Science; Multimedia Information Systems; Computer Communication Networks; Data Structures, Cryptology and Information Theory; Special Purpose and Application-Based Systems
ISSN
1380-7501
eISSN
1573-7721
D.O.I.
10.1007/s11042-017-4578-0
Publisher site
See Article on Publisher Site

Abstract

This paper proposes a realistic 3D articulatory animation system for emotional visual pronunciation driven by the emotional articulatory movement trajectory. Firstly, the articulatory movements, recorded by Electro-Magnetic Articulatory (EMA), are trained by Hidden Markov Model (HMM) while the fully context-dependent model is taken into account by making full use of the rich linguistic features. Secondly, owing to the independency in the manipulation of articulators, the articulatory movements are more remarkably adjusted to express different emotions. Thirdly, the emotional speech is generated by adjusting the neutral speech parameters, such as fundamental frequency (F0), duration and intensity, based on Praat. Then the corresponding articulatory movements are synthesized by the HMM prediction rules which are used to drive the head mesh model along with the emotional speech simultaneously. The experiments intend to synthesize accurate emotional speech synchronized animation of articulators based on the system at phoneme level.

Journal

Multimedia Tools and ApplicationsSpringer Journals

Published: Apr 21, 2017

References

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