Neural Sign Language Synthesis: Words Are Our Glosses
Jan Zelinka, Jakub Kanis
Abstract
This paper deals with a text-to-video sign language synthesis. Instead of direct video production, we focused on skeletal models production. Our main goal in this paper was to design a fully end-to-end automatic sign language synthesis system trained only on available free data (daily TV broadcasting). Thus, we excluded any manual video annotation. Furthermore, our designed approach even do not rely on any video segmentation. A proposed feed-forward transformer and recurrent transformer were investigated. To improve the performance of our sequence-to-sequence transformer, soft non-monotonic attention was employed in our training process. A benefit of character-level features was compared with word-level features. We focused our experiments on a weather forecasting dataset in the Czech Sign Language.