@inproceedings{granero-moya-etal-2026-zero,
title = "Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes",
author = "Granero Moya, Marcel and
Farrar{\'o}s, Carolina Del Corral and
Haro, Gloria and
Ballester, Coloma and
Marques, Ricardo",
editor = "Singh, Sanjeet and
Joshi, Abhinav and
Dattatreya, Pokala and
Artiaga, Keren and
Pujara, Khushi Jaydeep and
{\v{Z}}elezn{\'y}, Tom{\'a}{\v{s}} and
Gruber, Ivan and
Honz{\'i}k, Jakub and
Kamila, Sabyasachi and
Quiroga, Facundo Manuel and
Hr{\'u}z, Marek and
Hasanuzzaman, Mohammed and
Modi, Ashutosh",
booktitle = "Proceedings of the Second Workshop on Sign Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.wslp-1.18/",
pages = "177--192",
ISBN = "979-8-89176-488-0",
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."
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<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.</abstract>
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%0 Conference Proceedings
%T Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes
%A Granero Moya, Marcel
%A Farrarós, Carolina Del Corral
%A Haro, Gloria
%A Ballester, Coloma
%A Marques, Ricardo
%Y Singh, Sanjeet
%Y Joshi, Abhinav
%Y Dattatreya, Pokala
%Y Artiaga, Keren
%Y Pujara, Khushi Jaydeep
%Y Železný, Tomáš
%Y Gruber, Ivan
%Y Honzík, Jakub
%Y Kamila, Sabyasachi
%Y Quiroga, Facundo Manuel
%Y Hrúz, Marek
%Y Hasanuzzaman, Mohammed
%Y Modi, Ashutosh
%S Proceedings of the Second Workshop on Sign Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-488-0
%F granero-moya-etal-2026-zero
%X 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.
%U https://aclanthology.org/2026.wslp-1.18/
%P 177-192
Markdown (Informal)
[Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes](https://aclanthology.org/2026.wslp-1.18/) (Granero Moya et al., WSLP 2026)
ACL
- Marcel Granero Moya, Carolina Del Corral Farrarós, Gloria Haro, Coloma Ballester, and Ricardo Marques. 2026. Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes. In Proceedings of the Second Workshop on Sign Language Processing, pages 177–192, Budapest, Hungary. Association for Computational Linguistics.