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arXiv · 2411.16765

SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction

Shester Gueuwou·Xiaodan Du·Greg Shakhnarovich·Karen Livescu·Alexander H. Liu
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Abstract

Sign language processing has traditionally relied on task-specific models, limiting the potential for transfer learning across tasks. Pre-training methods for sign language have typically focused on either supervised pre-training, which cannot take advantage of unlabeled data, or context-independent (frame or video segment) representations, which ignore the effects of relationships across time in sign language. We introduce SHuBERT (Sign Hidden-Unit BERT), a self-supervised contextual representation model learned from approximately 1,000 hours of American Sign Language video. SHuBERT adapts masked token prediction objectives to multi-stream visual sign language input, learning to predict multiple targets corresponding to clustered hand, face, and body pose streams. SHuBERT achieves state-of-the-art performance across multiple tasks including sign language translation, isolated sign language recognition, and fingerspelling detection.

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Shester Gueuwou, Xiaodan Du, Greg Shakhnarovich, Karen Livescu, Alexander H. Liu. 2024-11-25. SHuBERT: Self-Supervised Sign Language Representation Learning via Multi-Stream Cluster Prediction. https://arxiv.org/abs/2411.16765

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