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AI models adapted for sign language turn-taking prediction

By PulseAugur Editorial · [1 sources] ·

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.

RANK_REASON Academic paper presenting an initial transfer study of adapting a VAP architecture to sign language interaction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

COVERAGE [1]

  1. arXiv cs.CL TIER_1 English(EN) · Kotaro Funakoshi ·

    Toward Signing Activity Projection in Sign Language Interaction

    Social robots must interact robustly not only with users assumed by speech-centered systems but also with diverse users whose communication relies on different modalities, e.g., sign language. One important capability gap is predictive turn-taking with signing users. Although Voi…

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