Abstract
Gestures during spoken dialog play a central role in human communication. As a consequence, models of gesture generation are a key challenge in research on virtual humans, embodied agents capable of face-to-face interaction with people. Machine learning approaches to gesture generation must take into account the conceptual content in utterances, physical properties of speech signals and the physical properties of the gestures themselves. To address this challenge, we proposed a gestural sign scheme to facilitate supervised learning and presented the DCNF model, a model to jointly learn deep neural networks and second order linear chain temporal contingency. The approach we took realizes both the mapping relation between speech and gestures while taking account temporal relations among gestures. Our experiments on human co-verbal dataset shows significant improvement over previous work on gesture prediction. A generalization experiment performed on handwriting recognition also shows that DCNFs outperform the state-of-the-art approaches.
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Notes
- 1.
We have experimented with both the logistic and the rectified linear (\(\max (a\theta ^{w}, 0)\)) functions with similar results. Because of space constraints, we are focusing on the logistic function.
- 2.
The full derivation of the gradient was omitted because of space constraint.
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The projects or effort described here has been sponsored by the U.S. Army. Any opinions, content or information presented does not necessarily reflect the position or the policy of the United States Government, and no official endorsement should be inferred.
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Chiu, CC., Morency, LP., Marsella, S. (2015). Predicting Co-verbal Gestures: A Deep and Temporal Modeling Approach. In: Brinkman, WP., Broekens, J., Heylen, D. (eds) Intelligent Virtual Agents. IVA 2015. Lecture Notes in Computer Science(), vol 9238. Springer, Cham. https://doi.org/10.1007/978-3-319-21996-7_17
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