Exploring Sign Language Dataset Augmentation with Generative Artificial Intelligence Videos: A Case Study Using Adobe Firefly-Generated American Sign Language Data
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| Publicado en: | Information vol. 16, no. 9 (2025), p. 799-817 |
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| Autor principal: | |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 022 | |a 2078-2489 | ||
| 024 | 7 | |a 10.3390/info16090799 |2 doi | |
| 035 | |a 3254540428 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231474 |2 nlm | ||
| 100 | 1 | |a Bercaru Valentin | |
| 245 | 1 | |a Exploring Sign Language Dataset Augmentation with Generative Artificial Intelligence Videos: A Case Study Using Adobe Firefly-Generated American Sign Language Data | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Case Study Journal Article | ||
| 520 | 3 | |a Currently, high quality datasets focused on Sign Language Recognition are either private, proprietary or difficult to obtain due to costs. Therefore, we aim to mitigate this problem by augmenting a publicly available dataset with artificially generated data in order to enrich and obtain a more diverse dataset. The performance of Sign Language Recognition (SLR) systems is highly dependent on the quality and diversity of training datasets. However, acquiring large-scale and well-annotated sign language video data remains a significant challenge. This experiment explores the use of Generative Artificial Intelligence (GenAI), specifically Adobe Firefly, to create synthetic video data for American Sign Language (ASL) fingerspelling. Thirteen letters out of 26 were selected for generation, and short videos representing each sign were synthesized and processed into static frames. These synthetic frames replaced approximately 7.5% of the original dataset and were integrated into the training data of a publicly available Convolutional Neural Network (CNN) model. After retraining the model with the augmented dataset, the accuracy did not drop. Moreover, the validation accuracy was approximately the same. The resulting model achieved a maximum accuracy of 98.04%. While the performance gain was limited (less than 1%), the approach illustrates the feasibility of using GenAI tools to generate training data and supports further research into data augmentation for low-resource SLR tasks. | |
| 653 | |a Machine learning | ||
| 653 | |a Datasets | ||
| 653 | |a Data augmentation | ||
| 653 | |a Accuracy | ||
| 653 | |a Deep learning | ||
| 653 | |a Sign language | ||
| 653 | |a Frames (data processing) | ||
| 653 | |a Computer vision | ||
| 653 | |a Video recordings | ||
| 653 | |a Recognition | ||
| 653 | |a Artificial neural networks | ||
| 653 | |a Generative artificial intelligence | ||
| 653 | |a Support vector machines | ||
| 653 | |a Video data | ||
| 653 | |a Engineering | ||
| 653 | |a Case studies | ||
| 700 | 1 | |a Popescu Nirvana | |
| 773 | 0 | |t Information |g vol. 16, no. 9 (2025), p. 799-817 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3254540428/abstract/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3254540428/fulltextwithgraphics/embedded/H09TXR3UUZB2ISDL?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3254540428/fulltextPDF/embedded/H09TXR3UUZB2ISDL?source=fedsrch |