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opencc-by-4.0Oct 2020View details →

The newly developed signs in South African Sign Language for medical terms regarding diabetes

New signs were developed to convey the meaning of eleven medical terms regarding diabetes. A visual containing stop motions to explain how these signs were developed is attached. The glossary in a video format can also be accessed from https://www.youtube.com/watch?v=Z2aI0zgNMik&list=PL_34qO4qYjUgElXu_Ku5fKpPznrbGYPGC. These new signs were part of the results of Co-design Activity 2.

opencc-by-4.0Dec 2023View details →

Nepali Sign Language -Consonant and Vowel

Nepali Sign language is used by the differently able Nepali speaking community of Sikkim, hilly regions of North Bengal, some parts of Uttrakhand, Meghalaya and Assam in India and the major part of Nepal. This dataset is the first Nepali Sign language dataset prepared to represent consonant and vowel signs in the form of videos.Dataset was collected with the help of 14 volunteers, categorized as 5 Native (>25 years and >5 years of using Nepali Sign Language) and 9 beginners learning Nepali Sign Language.Dataset has 630 total videos containing 1205 gestures for 36 consonants and 13 vowels of Nepali Sign Language out of which 516 Videos is for consonant that has 972 gestures and  114 videos for vowel that has 234 gestures. Data was collected in  two types of environment <ol><li>Prepared environment - Black background by putting a black chart paper at the background of the wall where the gesture is performed.</li><li>Unprepared environment- No black chart paper was used to change the background while performing the gesture.</li></ol> Real world environment indoor and outdoor has also been considered.Lighting condition – Natural lighting condition has been considered while capturing the data at indoor and outdoor i.e no extra light was focused on the volunteer performing the gesture. Bright and Dark condition for indoor videos are referring the videos captured by turning the room lights and off respectively.

opencc-by-4.0Mar 2024View details →

Video to accompany publication "A software editor for the AZVD graphical Sign Language representation system" (Filhol & Von Ascheberg, 2024)

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opencc-by-4.0Jun 2024View details →

A Computer Graphics Approach to Creating New Method for Generating 3D Gesture Animations in Bangla Sign Language via HamNoSys to SiGML Conversion

To prepare the system, we employed 94 classes of data. In this dataset, there are 13 Bangla numerical data classes, 36 Bangla alphabet data classes, and 41 Bangla word data classes. Every class of data contains different data types like Hand Shape, Hand Orientation, Hand Movement, Notations, etc. These data were created in SiGML tags. Every class of data is unique and different from others. The system was prepared using BdSL. And BdSL is an uncommon and unique sign language, among others. That's why every class of data is unique and created by us. We search HamNoSys notations for Bangla alphabets, words, and numbers in English HamNoSys datasets (almost 6,000 data). However, we find only 20% of the data, which is quite similar to BdSL. We create 80% HamNoSys notation for BdSL and we modify the matching 20% notations. Then, we converted them into SiGML and made the data classes. This was a big challenge in our research.

ClinicalTrials.gov28/100

Areas of Brain Responsible for Understanding American Sign Language

ClinicalTrials.gov study NCT00001782. IPD Sharing: Not stated. Countries: 1. Publications: 2.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov28/100

American Sign Language-Accessible Diabetes Education

ClinicalTrials.gov study NCT03980808. IPD Sharing: YES. Countries: 1. Publications: 0.

ClinicalTrials.gov24/100

Recognition of Second Language Spoken Words, Signs, and Characters Via Perception and Production in Adults

ClinicalTrials.gov study NCT05587218. IPD Sharing: YES. Countries: 1. Publications: 0.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov24/100

Mini-Mental State (MMS-LS) and Sign Language

ClinicalTrials.gov study NCT02005679. IPD Sharing: Not stated. Countries: 1. Publications: 0.

restrictedcc-by-4.0Feb 2024View details →

zenodo20/100

Annotation Scheme for Continuous Data in Swiss German Sign Language

Annotation scheme described in the paper "Advancing Annotation for Continuous Data in Swiss German Sign Language" to annotate sentences in Swiss German Sign Language. The annotations are in both German and English.

zenodo20/100

WMT-SLT SRF: Training data for the WMT shared task on sign language translation (videos, subtitles)

restrictedJun 2022View details →

zenodo20/100

"PECULIARITIES OF THE TRANSMISSION OF SIGN LANGUAGE TO PARENTS OF CONGENITALLY DEAF CHILDREN"

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opencc-by-4.0Oct 2024View details →

ClinicalTrials.gov20/100

Adaptation and Normalization of a Verbal Episodic Memory Test in French Sign Language

ClinicalTrials.gov study NCT06902753. IPD Sharing: Not stated. Countries: 0. Publications: 0.

ClinicalTrials.gov20/100

Language Therapy in British Sign Language

ClinicalTrials.gov study NCT02579668. IPD Sharing: Not stated. Countries: 0. Publications: 0.

restrictedFeb 2022View details →

FluentSigners-50: a signer independent benchmark dataset for Sign Language Processing

A new large-scale Kazakh-Russian Sign Language dataset (FluentSigners-50) as a new Continuous Sign Language Recognition benchmark.  FluentSigners-50 proposes to address three shortcomings of commonly used datasets: continuous signing, signer variety, and native signers. FluentSigners-50's main advantage is in its large signer variety: age (ranging from 8 to 57 years old), gender (18 male and 32 female), clothing, skin tone, body proportions, disability (deaf or hard of hearing), and fluency. Additionally, as the dataset was crowd-sourced: the participants were using a variety of their own recording devices (such as smartphones and web cameras), it resulted in a large variety of backgrounds, lighting conditions, camera quality, frame rates, camera aspect ratios, and angles. Finally, FluentSigners-50 contains recordings of 50 contributors that use sign language on a daily basis: either deaf, hard of hearing, hearing CODA (Child of Deaf Adults), and hearing SODA (Sibling of a Deaf Adult). As a result, the dataset contains a high degree of linguistic variability, including phonetic, phonological, lexical, and syntactic variations. It thus is a better training set for recognition of natural signing. The FluentSigners-50 dataset consists of everyday conversational phrases and sentences in KRSL, the sign language used in the Republic of Kazakhstan. KRSL is closely related to Russian Sign Language (RSL) and some other sign languages of the ex-Soviet Union. While no official research comparing KRSL with RSL exists, our observations based on our experience researching both languages are that they show a substantial lexical overlap and are entirely mutually intelligible. The sentences and phrases of FluentSigners-50 represent the following sentence types: statements, polar questions, wh-questions, and requests. All FluentSigners-50 contributors use sign language on a daily basis as they are either deaf (N=32), hard of hearing (N=6), hearing SODA (N=3), or hearing CODA (N=9). Native signers are signers who have been exposed to signed languages since birth because their parents are deaf. While the early acquisition may be necessary for the development of native language abilities, other factors, particularly the quality of language input, may play a role. According to this distinction, FluentSigners-50 has 30 CODA contributors (including nine hearing signers) and 20 who are not CODA (16 deaf, one hard of hearing, and three hearing SODA). Nevertheless, we decided to name our dataset FluentSigners-50 because all of our contributors use sign language daily, and it is their primary language of communication. They all came from various regions of Kazakhstan and are of different age and gender groups.

restrictedcc-by-4.0Jul 2024View details →

Dataset for Usability Study of a Sign Language Learning Tool

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restrictedMar 2023View details →

SMILE Swiss German Sign Language Dataset

Description The SMILE Swiss German Sign Language Dataset consists of videos, joint coordinates and annotations of 100 isolated signs of a Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) vocabulary production test. All items were produced multiple times by 16 adult L1 signers and 22 adult L2 learners of DSGS. Associated linguistic transcriptions and annotations are available for second path data of 10 adult L1 signers and 18 adult L2 learners of DSGS. The dataset has been created in the context of developing an assessment system for lexical signs of DSGS in the SNSF project SMILE. More precisely, for each participant, the following files are available: <ul> <li>Kinect color video (.mp4); 1920x1080 Pixels @ 30 FPS;</li> <li>Kinect Pose Information (.csv); 25 Joints; 3D Joint Coordinates and Angles;</li> <li>OpenPose output (.json); 2D Joint Coordinates and Confidences;</li> <li>iLex annotation files (.xml); linguistic annotations.</li> </ul>   Reference If you use this database, please cite the following publication: Sarah Ebling, Necati Cihan Camgöz, Penny Boyes Braem, Katja Tissi, Sandra Sidler-Miserez, Stephanie Stoll, Simon Hadfield, Tobias Haug, Richard Bowden, Sandrine Tornay, Marzieh Razavi, and Mathew Magimai-Doss. SMILE Swiss German Sign Language Dataset. In Proceedings of the 11th Language Resources and Evaluation Conference (LREC 2018), pages 4221–4229, 2018.