This study introduces the continuous Educational Turkish Sign Language (E-TSL) dataset, collected from online Turkish language lessons for 5th, 6th, and 8th grades. The dataset comprises 1,410 videos totaling nearly 24 hours and includes performances from 11 signers. Turkish, an agglutinative language, poses unique challenges for sign language translation, particularly with a vocabulary where 64% are singleton words and 85% are rare words, appearing less than five times. We developed two baseline models to address these challenges: the Pose to Text Transformer (P2T-T) and the Graph Neural Network based Transformer (GNN-T) models. The GNN-T model achieved 19.13% BLEU-1 score and 3.28% BLEU-4 score, presenting a significant challenge compared to existing benchmarks. The P2T-T model, while demonstrating slightly lower performance in BLEU scores, achieved a higher ROUGE-L score of 22.09%. Additionally, we benchmarked our model using the well-known PHOENIX-Weather 2014T dataset to validate our approach.

这项研究引入了连续的教育土耳其手语（E-TSL）数据集，收集自第5、6和8年级的在线土耳其语课程。该数据集包含1,410个视频，总计近24小时，包括来自11位手语演讲者的表演。我们开发了两种基准模型来解决这些挑战：姿势到文本转换器（P2T-T）和基于图神经网络的Transformer（GNN-T）模型。GNN-T模型在BLEU-1得分上达到19.13％，BLEU-4得分为3.28％，与现有基准相比提出了显著挑战。P2T-T模型在BLEU得分方面略有下降，但在ROUGE-L得分方面达到了更高的22.09％。此外，我们使用着名的PHOENIX-Weather 2014T数据集对我们的方法进行了验证。

E-TSL：具备基线方法的连续性教育土耳其手语数据集