为资源匮乏的手语开发识别与翻译系统，推动无障碍沟通
Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward
- 以阿塞拜疆手语为案例，推动社区共建的数据采集与标注
- 提出从架构导向转向数据驱动、个性化适应的新范式
- 适合手语技术研究者及残障包容性科技开发者参考
手语是全球聋人群体使用的自然视觉-手势语言。超过300种手语仍属严重低资源状态，受限于文献匮乏、数据集稀疏和计算工具不足。本文系统综述了针对低资源手语的识别与翻译研究，以阿塞拜疆手语（AzSL）为例，分析全球倡议提炼出八项可操作经验，包括社区共同设计、方言多样性捕捉及隐私保护的基于姿态的表征。重点关注突厥语系手语（哈萨克、土耳其、阿塞拜疆），因其语言相似性利于迁移学习。提出三大范式转变：由架构中心转向数据中心、由无身份依赖转向身份自适应系统、由参考基准评估转向任务特定指标。为AzSL制定技术路线图，采用轻量级MediaPipe架构、社区验证标注及离线优先部署。持续跨学科合作需以聋人群体为中心，确保文化真实性、伦理治理与实际交流效益。
原文摘要 · Abstract (English)
Sign languages are natural, visual-gestural languages used by Deaf communities worldwide. Over 300 distinct sign languages remain severely low-resource due to limited documentation, sparse datasets, and insufficient computational tools. This systematic review synthesizes literature on sign language recognition and translation for under-resourced languages, using Azerbaijan Sign Language (AzSL) as a case study. Analysis of global initiatives extracts eight actionable lessons, including community co-design, dialectal diversity capture, and privacy-preserving pose-based representations. Turkic sign languages (Kazakh, Turkish, Azerbaijani) receive special attention, as linguistic proximity enables effective transfer learning. We propose three paradigm shifts: from architecture-centric to data-centric AI, from signer-independent to signer-adaptive systems, and from reference-based to task-specific evaluation metrics. A technical roadmap for AzSL leverages lightweight MediaPipe-based architectures, community-validated annotations, and offline-first deployment. Progress requires sustained interdisciplinary collaboration centered on Deaf communities to ensure cultural authenticity, ethical governance, and practical communication benefit.
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