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Grounding Sign Language Representation Learning in Phonology

Sign language recognition systems are commonly trained using gloss-level supervision, treating signs as holistic lexical units. While effective for classification, such approaches entangle sub-lexical structure and fail to capture the phonological parameters that govern sign formation, limiting interpretability, robustness, and cross-lingual transfer. In this work, we propose a phonologically informed representation learning architecture that explicitly structures the latent space according to linguistic principles. Grounded in the Dependency Model – a phonological model used to describe Flemish Sign Language (VGT) – our hierarchical architecture disentangles parameter-specific subspaces for handshape and location and is trained with multi-label phoneme supervision. To evaluate whether phonological information is directly encoded in the geometry of the embedding space, we introduce a non-parametric probing method that measures neighbourhood consistency across increasing scales. We show that conventional gloss-based networks achieve reasonable performance only for very small neighbourhoods, reflecting incidental visual similarity. In contrast, our disentangled representations maintain stable performance for larger neighbourhoods. This behaviour indicates that phonological structure is preserved across broader regions of the space, yielding more coherent and robust embeddings. Together, our results show that explicit phonological supervision – and crucially, disentangled representation learning – provides a principled foundation for interpretable and transferable sign language representations. Keywords: Sign Language, Machine Learning