构建首个叙利亚阿拉伯手语数据集，助力聋哑人群信息平等
SyriSign: A Parallel Corpus for Arabic Text to Syrian Arabic Sign Language Translation
- 构建1500个视频的平行语料库，覆盖150个独特手语词汇
- 生成模型在有限数据下展现手语表征潜力，但泛化能力受限
- 适合手语翻译、无障碍技术研究者使用
手语是听障人士（DHH）的主要交流方式。尽管高资源手语已有多个基准数据集，但阿拉伯语等低资源手语仍严重缺乏。目前尚无公开的叙利亚阿拉伯手语（SyArSL）数据集。为此，我们提出SyriSign，包含1500个视频样本、覆盖150个独特词汇的语料库，用于文本到SyArSL翻译任务。该研究旨在减少叙利亚的信息沟通障碍——因多数新闻以口语或书面阿拉伯语传播，对听障群体不友好。我们采用三种深度学习架构评估：MotionCLIP（语义动作生成）、T2M-GPT（文本条件动作合成）、SignCLIP（双语嵌入对齐）。实验表明，生成式方法虽具潜力，但受限于数据规模，泛化性能不足。我们将公开发布SyriSign，期望其成为该领域的首个基准。
原文摘要 · Abstract (English)
Sign language is the primary approach of communication for the Deaf and Hard-of-Hearing (DHH) community. While there are numerous benchmarks for high-resource sign languages, low-resource languages like Arabic remain underrepresented. Currently, there is no publicly available dataset for Syrian Arabic Sign Language (SyArSL). To overcome this gap, we introduce SyriSign, a dataset comprising 1500 video samples across 150 unique lexical signs, designed for text-to-SyArSL translation tasks. This work aims to reduce communication barriers in Syria, as most news are delivered in spoken or written Arabic, which is often inaccessible to the deaf community. We evaluated SyriSign using three deep learning architectures: MotionCLIP for semantic motion generation, T2M-GPT for text-conditioned motion synthesis, and SignCLIP for bilingual embedding alignment. Experimental results indicate that while generative approaches show strong potential for sign representation, the limited dataset size constrains generalization performance. We will release SyriSign publicly, hoping it serves as an initial benchmark.
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