This article introduces the parallel sign language translation corpus, PaSCo1, developed as part of the BabelDr project, an automatic speech translation system for medical triage. PaSCo1 aims to make a set of medical data available in Swiss French Sign Language (LSF-CH) in the form of both videos signed by a human and their description in G-SiGML mark-up language. We describe the beginnings of the corpus as part of the BabelDr project, as well as the methodology used to create the videos and generate the G-SiGML language using the SiGLA platform. The resulting FAIR corpus comprises 2 031 medical questions and instructions in the form of videos and G-SiGML code.

This paper presents a study on Swiss-French sign language production in the medical domain. In emergency care settings, a lack of clear communication can interfere with accurate delivery of health related services. For patients communicating with sign language, equal access to healthcare remains an issue. While previous work has explored producing sign language gloss from a source text, we propose to extend this approach to produce a multichannel sign language output given a written French input. Furthermore, we extend our approach with a multi-task framework allowing us to include the Unified Medical Language System (UMLS) in our model. Results show that the introduction of UMLS in the training data improves model accuracy by 13.64 points.