对比LSTM与新模型，提升手语识别准确率至96%以上。
Attention vs LSTM: Improving Word-level BISINDO Recognition
- 用1D CNN+Transformer融合关键点序列特征，增强复杂手势识别能力。
- 新模型准确率达96.12%，优于LSTM的94.67%，尤其在相似手势上表现更稳。
- 适合用于公共设施中的无障碍手语翻译系统，支持50种手势快速识别。
印度尼西亚是全球听力障碍患者第四多的国家。听障人士常面临沟通困难，亟需手语服务。本文探讨人工智能在简化手语翻译应用与词典开发中的应用，旨在推动其在公共服务场所的集成，提升无障碍水平。研究比较了LSTM与1D CNN + Transformer（1DCNNTrans）模型在手语识别中的表现。实验结果显示，LSTM模型准确率为94.67%，而1DCNNTrans模型达到96.12%。尽管LSTM推理延迟更低，但在具有相似关键点的手势分类上表现较弱；相比之下，1DCNNTrans在复杂度不同的类别上均表现出更高的稳定性和F1分数。两种模型验证准确率均超过90%，可实现50种手语手势的快速分类。
原文摘要 · Abstract (English)
Indonesia ranks fourth globally in the number of deaf cases. Individuals with hearing impairments often find communication challenging, necessitating the use of sign language. However, there are limited public services that offer such inclusivity. On the other hand, advancements in artificial intelligence (AI) present promising solutions to overcome communication barriers faced by the deaf. This study aims to explore the application of AI in developing models for a simplified sign language translation app and dictionary, designed for integration into public service facilities, to facilitate communication for individuals with hearing impairments, thereby enhancing inclusivity in public services. The researchers compared the performance of LSTM and 1D CNN + Transformer (1DCNNTrans) models for sign language recognition. Through rigorous testing and validation, it was found that the LSTM model achieved an accuracy of 94.67%, while the 1DCNNTrans model achieved an accuracy of 96.12%. Model performance evaluation indicated that although the LSTM exhibited lower inference latency, it showed weaknesses in classifying classes with similar keypoints. In contrast, the 1DCNNTrans model demonstrated greater stability and higher F1 scores for classes with varying levels of complexity compared to the LSTM model. Both models showed excellent performance, exceeding 90% validation accuracy and demonstrating rapid classification of 50 sign language gestures.
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