Attention vs LSTM: Improving Word-level BISINDO Recognition

• URL: http://arxiv.org/abs/2409.01975v2
• Date: Fri, 07 Feb 2025 04:53:48 GMT
• Title: Attention vs LSTM: Improving Word-level BISINDO Recognition
• Authors: Muchammad Daniyal Kautsar, Afra Majida Hariono, Ridwan Akmal,
• Abstract summary: Indonesia ranks fourth globally in the number of deaf cases.<n>Individuals with hearing impairments often find communication challenging, necessitating the use of sign language.<n>This study aims to explore the application of AI in developing models for a simplified sign language translation app and dictionary.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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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