arXiv:2606.11925v1 Announce Type: cross Abstract: Sign language translation (SLT) converts sign language video into spoken language text and holds significant promise for improving accessibility and enabling communication between signing and non-signing communities. While large weakly-aligned datasets have enabled pre-training at scale and gloss-free methods have reduced reliance on expert annotation, high-quality parallel sign video-text pairs for fine-tuning remain scarce, limiting generalisation on long-tail vocabulary and unseen constructions. We propose a corpus augmentation approach that
The proliferation of powerful large language models (LLMs) and advancements in video processing are enabling new approaches to data synthesis and augmentation, directly addressing long-standing data scarcity issues in specialized AI domains like sign language translation.
Improving sign language translation enhances accessibility for millions globally, fosters greater inclusion, and demonstrates the transformative potential of AI to bridge communication gaps via innovative data generation techniques.
The ability to generate high-quality synthetic data for sign language translation could accelerate the development of more robust and generalized SLT systems, reducing reliance on expensive and scarce expert annotations and expanding vocabulary coverage.
- · Deaf and hard-of-hearing communities
- · AI researchers in low-resource language domains
- · Accessibility technology developers
- · Large language model providers
More accurate and versatile sign language translation tools become available.
Increased integration of SLT into common communication platforms and devices, expanding real-time interaction capabilities.
The success in SLT data augmentation inspires similar LLM-guided synthetic data generation efforts across other multimodal and low-resource domains, accelerating AI development in diverse fields.
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Read at arXiv cs.LG