arXiv:2606.28667v1 Announce Type: new Abstract: Sign languages are compositional systems where meaning arises by combining sublexical phonological parameters, such as handshape, location, and movement. While deep learning models for Sign Language Recognition (SLR) have achieved increased performance on translation benchmarks, it remains unclear whether these models distinguish abstract phonological features or merely rely on low-level statistical correlations. This work evaluates the phonological perception of SLR models trained on American Sign Language (ASL) by probing phonological sensitivi
The increasing performance of deep learning models in Sign Language Recognition (SLR) necessitates a deeper understanding of their perceptual capabilities beyond surface-level metrics.
Evaluating whether AI models understand abstract phonological features of sign languages, rather than just statistical correlations, is crucial for developing truly intelligent and robust multilingual AI systems.
This research provides a methodology to probe the phonological perception of SLR models, potentially enabling the development of more nuanced and culturally informed AI applications for sign language interpretation.
- · AI researchers
- · Deaf community
- · Machine learning ethics
- · Sign language educators
- · Developers of superficial SLR models
Improved understanding of internal workings and limitations of Sign Language Recognition models.
Development of next-generation SLR models that are more linguistically sound and less prone to bias.
Enhanced AI accessibility for deaf communities and greater integration of sign languages into AI-powered communication tools.
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