首个孟加拉手语翻译数据集，助力聋哑人群智能辅助工具研发
Bangla Sign Language Translation: Dataset Creation Challenges, Benchmarking and Prospects
- 构建孟加拉手语数据集IsharaKhobor及子集，支持研究与评测
- 通过关键点与嵌入表征基准测试，验证模型性能上限
- 提供小规模与规范化版本，适配资源受限场景
孟加拉手语翻译（BdSLT）因语言资源极度匮乏而发展受限。构建标准句子级数据集对开发面向孟加拉语聋哑人群的AI辅助工具至关重要。本文提出新数据集IsharaKhobor及其两个子集，以推动相关研究。我们分析了数据集构建中的挑战，并通过基于关键点的原始数据与RQE嵌入表征进行基准测试。此外，对词汇限制与规范化进行了消融实验，衍生出IsharaKhobor_small与IsharaKhobor_canonical_small两个新数据集。数据集已公开于：www.kaggle.com/datasets/hasanssl/isharakhobor [1]。
原文摘要 · Abstract (English)
Bangla Sign Language Translation (BdSLT) has been severely constrained so far as the language itself is very low resource. Standard sentence level dataset creation for BdSLT is of immense importance for developing AI based assistive tools for deaf and hard of hearing people of Bangla speaking community. In this paper, we present a dataset, IsharaKhobor , and two subset of it for enabling research. We also present the challenges towards developing the dataset and present some way forward by benchmarking with landmark based raw and RQE embedding. We do some ablation on vocabulary restriction and canonicalization of the same within the dataset, which resulted in two more datasets, IsharaKhobor_small and IsharaKhobor_canonical_small. The dataset is publicly available at: www.kaggle.com/datasets/hasanssl/isharakhobor [1].
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