arXiv:2510.23842v2 Announce Type: replace Abstract: Most state-of-the-art sign language models are trained on interpreter or isolated vocabulary data, which overlooks the variability that characterizes natural dialogue. However, human communication dynamically adapts to contexts and interlocutors through spatiotemporal changes and articulation style. This specifically manifests itself in educational settings, where novel vocabularies are used by teachers, and students. To address this gap, we collect a motion capture dataset of American Sign Language (ASL) STEM (Science, Technology, Engineerin
The increasing sophistication of AI models and demand for richer, more natural human-computer interaction highlights the limitations of current sign language datasets.
This research is crucial for advancing AI's ability to understand and generate natural sign language, which is vital for accessibility and more intuitive interactions in diverse communication contexts.
The development of motion capture datasets for natural sign language directly addresses a critical data gap, paving the way for more robust and context-aware sign language AI.
- · AI researchers in sign language processing
- · Deaf and hard-of-hearing communities
- · Educational technology providers
- · Natural Language Processing (NLP) sector
- · Developers relying solely on limited, isolated vocabulary datasets
- · AI tools lacking nuanced sign language understanding
Improved sign language recognition and generation models for AI applications.
Enhanced accessibility tools and educational resources, particularly in specialized fields like STEM, for deaf individuals.
Potential for new human-computer interfaces based on natural gestural communication, extending beyond just sign language.
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