Unlocking Sign Language with AI: A Breakthrough in Pseudo-Annotation

AI & Machine LearningAI Diagnostics

Imagine a world where sign language interpretation is as seamless as spoken language. Researchers are making this a reality with their innovative pseudo-annotation pipeline.

TL;DR
• Researchers developed a pseudo-annotation pipeline for sign language interpretation.
• The pipeline uses sparse predictions from fingerspelling and ISR models.
• The pipeline achieves state-of-the-art results on FSBoard and ASL Citizen datasets.

Summary
Sign language AI research faces a critical data bottleneck. High‑quality annotations are scarce because manual labeling is expensive, limiting model performance and deployment.
• Deploy pseudo‑annotation pipelines that combine sparse recognizers with few‑shot language models to generate time‑aligned glosses, classifiers, and fingerspelled words.
• Release annotated datasets and pseudo‑annotations publicly to accelerate community progress and enable reproducibility.
• Benchmark new models on standard datasets (FSBoard, ASL Citizen) to establish state‑of‑the‑art baselines.
• Engage professional interpreters for gold‑standard validation, ensuring cultural and linguistic accuracy.
• Integrate continuous learning loops where model predictions are refined through human feedback, reducing annotation costs over time.

The proposed pipeline accepts signed video and English transcripts, producing ranked annotation candidates with temporal boundaries. It leverages a fingerspelling recognizer, an isolated sign recognizer, and a K‑Shot large language model to estimate gloss sequences. Baseline models achieve 6.7% character error rate on FSBoard and 74% top‑1 accuracy on ASL Citizen, surpassing previous results.

A professional interpreter annotated nearly 500 ASL STEM Wiki videos, adding sequence‑level gloss labels that include classifiers and fingerspelled words. These human annotations, combined with over 300 hours of pseudo‑annotations, are released as supplemental material, providing a comprehensive benchmark for future research.

The work demonstrates that combining lightweight recognizers with advanced language models can bootstrap large‑scale sign language datasets, reducing reliance on costly manual labor while maintaining high annotation quality. This approach supports scalable, inclusive AI systems that serve the Deaf and Hard‑of‑Hearing community.

As we continue to push the boundaries of AI-driven sign language interpretation, we must remember the importance of accessibility and inclusivity in our technological advancements.