arXiv cs.CL· Nigar Alishzade, Gulchin Abdullayeva·· 12 小时前AI 评分8
低资源语言的手语识别与翻译：挑战与前进路径
Sign Language Recognition and Translation for Low-Resource Languages: Challenges and Pathways Forward
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一篇系统综述梳理了 300 多种严重低资源手语的手语识别与翻译研究，并以阿塞拜疆手语（AzSL）为案例。文章从全球实践中提炼出社区共同设计、方言多样性捕捉、隐私保护的姿态表示等八条经验，并提出从架构中心转向数据中心、从签名者无关转向签名者自适应、从参考指标转向任务特定评估三项范式转变。该投稿已被 arXiv Admin 以不符合研究内容质量标准为由撤回。
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Abstract: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.
| Comments: | arXiv admin note: This submission has been withdrawn because it does not meet arXiv's research content quality standards |
| Subjects: | Computation and Language (cs.CL) |
| ACM classes: | I.4.8 |
| Cite as: | arXiv:2605.12096 [cs.CL] |
| (or arXiv:2605.12096v2 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2605.12096 arXiv-issued DOI via DataCite |
Submission history
From: arXiv Admin [view email]
[v1]
Tue, 12 May 2026 13:13:56 UTC (144 KB)
[v2]
Wed, 4 Nov 2026 22:00:14 UTC (1 KB) (withdrawn)
来源：arXiv cs.CL · arxiv.org