Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes

2026-09-16

AI Summary

本文通过将手形分解为五个音韵特征并使用零样本跨语言框架，从ASL转移到加泰罗尼亚手语，解决了低资源手语缺乏注释的问题。

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.

Problem

• Zero-Shot Cross-Lingual
• Sign Language Handshapes
• Phonological Features
• Low-Resource Languages

Innovation

• zero-shot cross-lingual
• handshape recognition
• phonological features
• composite phonological distance metric