Toward Signing Activity Projection in Sign Language Interaction
Researchers are exploring the adaptation of Voice Activity Projection (VAP) models to predict turn-taking in sign language interactions. An initial study using the Public DGS Corpus adapted a VAP architecture to sign language, utilizing pose data from hands and facial regions. While the model showed promise in predicting SHIFT/HOLD actions, particularly with hand cues, predicting the precise SHIFT remains challenging, indicating a need for sign-language-specific event definitions. AI
IMPACT This research could lead to more intuitive human-robot interaction for sign language users, improving accessibility in AI systems.