手语手形的零样本跨语言识别
Zero-Shot Cross-Lingual Recognition of Sign Language Handshapes
Marcel Granero-Moya, Carolina del Corral Farrarós, Gloria Haro, Coloma Ballester, Ricardo Marques
AI 速读 基于摘要
作者提出首个手形零样本跨语言框架，将 ASL 模型迁移到加泰罗尼亚手语（LSC），并借助共享的音系特征分解实现无目标语言训练标签的识别。
cs.CL · cs.CV
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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.