Proform structures such as classifier predicates have traditionally challenged Sign Language (SL) synthesis systems, particularly in respect to the production of smooth natural motion. To address this issue a synthesizer must necessarily leverage a structured linguistic model for such constructs to specify the linguistic constraints, and also an animation system that is able to provide natural avatar motion within the confines of those constraints. The proposed system bridges two existing technologies, taking advantage of the ability of AZee to encode both the form and functional linguistic aspects of the proform movements and on the Paula avatar system to provide convincing human motion. The system extends a previous principle that more natural motion arises from leveraging knowledge of larger structures in the linguistic description.
@InProceedings{FILHOL18.18024, author = {Michael Filhol and John McDonald}, title = {Extending the AZee-Paula Shortcuts to Enable Natural Proform Synthesis}, booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {may}, date = {7-12}, location = {Miyazaki, Japan}, editor = {Mayumi Bono and Eleni Efthimiou and
Stavroula-Evita Fotinea and Thomas Hanke and
Julie Hochgesang and Jette Kristoffersen and
Johanna Mesch and Yutaka Osugi}, publisher = {European Language Resources Association (ELRA)}, address = {Paris, France}, isbn = {979-10-95546-01-6}, language = {english} }
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