Isolated Sign Language Recognition for Icelandic Sign Language: Experiments in a Low-resource Setting
Read the original on arXiv Computation and Language →The Flow has not summarised this story yet — read it at arXiv Computation and Language.
The Flow has not summarised this story yet — read it at arXiv Computation and Language.
We present the first experiments on isolated sign language recognition (ISLR) for Icelandic Sign Language (ÍTM). We use ÍTM SignWiki, a dataset derived from a bilingual Icelandic--ÍTM online dictionar...
arXiv:2609.18772v1 Announce Type: new Abstract: Sign language processing advances rapidly for high-resource languages such as American Sign Language (ASL), yet most of the world's sign languages lack...
arXiv:2608. 06407v1 Announce Type: cross Abstract: Automated Sign Language Recognition for under-represented languages remains a largely unsolved problem.
arXiv:2605. 01720v3 Announce Type: replace-cross Abstract: Existing large-scale sign language resources typically provide supervision only at the level of raw video-text alignment and are often produced in laboratory settings.
arXiv:2606. 19352v1 Announce Type: cross Abstract: Sign languages are expressive visual languages used by Deaf and Hard-of-Hearing (DHH) communities.
SignBind-LLM introduces a modular framework for sign language translation that separates continuous signing, fingerspelling, and lipreading into dedicated expert streams. Each expert is pre‑trained independently on about two million pseudo‑gloss sequences, eliminating the need for manual gloss annotation. A lightweight transformer fuses the expert outputs, and a pre‑trained language model converts the fused pseudo‑glosses into fluent English, achieving state‑of‑the‑art performance on multiple benchmarks with lower training cost.