[2606.28667] Phonological Perception of Sign Language Models
[Submitted on 27 Jun 2026]
Title:Phonological Perception of Sign Language Models
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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 sensitivity using minimal pairs and evaluating representational alignment with human behavioral data. Our results reveal that SLR models exhibit emergent phonological sensitivity, but with clear architectural trade-offs: pose-based models are sensitive to handshape contrasts, while pixel-based models better capture location changes. Furthermore, pose-based models learn latent representations that correlate with human perceptual similarity judgments (r~0.49). These findings suggest that while SLR models exhibit emergent phonology, current training paradigms are insufficient to scale them beyond their architectural inductive biases.
Comments: Accepted to CogSci 2026
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2606.28667 [cs.CL]
(or arXiv:2606.28667v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2606.28667
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Kayo Yin [view email] [v1] Sat, 27 Jun 2026 01:02:35 UTC (7,695 KB)
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