arXiv cs.CL· Thapelo Sindane, Unarine Netshifhefhe, Vukosi Marivate, Abiodun Modupe, Herkulaas Combrink, Ketro Sithole·· 11 小时前AI 评分24
Maswao：面向南非手语的自动采集、制备与基准测试数据集
Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language
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研究团队提出一套几乎无需人工干预的手语数据集自动采集与制备框架，并以南非手语（SASL）为试验对象构建了 Maswao 数据集：从新闻广播等公开资源采集视频，用 ASR 工具将音频转为文本，裁剪手语区域并通过梯度下降定位非运动低谷以切分句子，形成弱句子对齐平行语料。
正文 · 原文
Abstract:Recent advancements in sign language processing can be attributed to developments in both large-scale dataset collection and algorithmic complexity in areas such as pose estimation techniques, signal processing through Transformer models, and model optimization. However, only a handful of sign languages, such as the American, British, and German, from over 200 globally recognized sign languages, reap the benefits of such advancements, while the rest lag behind. In this study, we aim to address this technological disparity by proposing a framework for the automatic collection and preparation of sign language datasets, with very minimal human-labour intervention, using the South African Sign Language (SASL) as a testbed. Specifically, we automatically collect SASL video content using public resources such as news broadcasts. Additionally, we convert their accompanying audio streams into text using available Automatic Speech Recognition (ASR) tools. Furthermore, we crop the sign language area in the videos and use gradient descent to locate valleys of non-motion to determine end-of-sentence points within long videos to create our weakly sentence-aligned parallel corpora -- the Maswao (Signs) dataset. Finally, we benchmark the weakly aligned dataset on the task of Sign-to-text Translation using Recurrent Neural Networks and Transformer models to compare their individual intricacies. Our results show that Transformer models proposed in the literature outperform recurrent baselines, with the best-performing Transformer achieving BLEU-1 to BLEU-4 scores of 13.78, 5.21, 2.52, and 1.54, respectively, as well as ChrF = 18.09 and ROUGE-L = 10.51. These results indicate that the task remains promising and warrants further investigations.
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2611.02903 [cs.CL] |
| (or arXiv:2611.02903v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2611.02903 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Thapelo Sindane [view email]
[v1]
Mon, 2 Nov 2026 10:36:22 UTC (2,562 KB)
来源：arXiv cs.CL · arxiv.org