Papers by Alessa Carbo
Improving Handshape Representations for Sign Language Processing: A Graph Neural Network Approach (2025.emnlp-main)
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| Challenge: | Existing systems for sign language recognition process a signing sequence holistically, leaving handshape information implicit, which limits both recognition accuracy and linguistic analysis. |
| Approach: | They propose a graph neural network that separates temporal dynamics from static handshape configurations in continuous signing sequences. |
| Outcome: | The proposed approach achieves 46% accuracy across 37 handshape classes, compared to 25% for baseline methods. |