Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes

Abstract:Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world's sign languages lack the phonological annotations new methods require. We present the first zero-shot cross-lingual framework for handshape recognition, transferring from ASL to Catalan Sign Language (LSC). Our approach leverages the decomposition of handshapes into five phonological features -- selected fingers, flexion, spread, thumb position, and thumb contact -- shared across both languages, to decode LSC handshapes from predicted features via a composite phonological distance metric. We evaluate three architectures (MLP, SL-GCN, SHuBERT) trained on two ASL corpora (PopSign, Sem-Lex) against a 37-handshape, single-signer LSC benchmark. Zero-shot transfer proves viable once recording-format disparities are harmonized, reaching 80.0% phonological feature accuracy and 54.5% expected handshape accuracy. Phonological decomposition thus offers a bridge for extending sign language technologies to low-resource languages without any target-language video training labels.

Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV)

arXiv:2609.18772 [cs.CL]

(or arXiv:2609.18772v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2609.18772 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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