@inproceedings{schiefner:26019:sign-lang:lrec, author = {Schiefner, Annika and Otterspeer, Gom{\`e}r and S{\"u}mer, Beyza and Roelofsen, Floris}, title = {The {SignBeach} Dataset of {Dutch} {Sign} {Language} ({NGT}) signs}, pages = {450--458}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC2026} 12th Workshop on the Representation and Processing of Sign Languages: Language in Motion}, maintitle = {15th International Conference on Language Resources and Evaluation ({LREC} 2026)}, publisher = {{ELRA Language Resources Association (ELRA)}}, address = {Palma, Mallorca, Spain}, day = {16}, month = may, year = {2026}, isbn = {978-2-493814-82-1}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/26019.html}, abstract = {This paper presents the SignBeach dataset, including 1401 lexical signs from Dutch Sign Language (NGT). The items in this dataset represent everyday vocabulary appropriate for primary school children and are part of a larger research project, investigating sign learning in a digital environment. Each sign is presented by four deaf signers in a controlled studio environment. For each item, high quality video recordings are available from five synchronised cameras, providing rich multi-view visual input suitable for linguistic analysis and the development of computer vision pipelines. In addition, we provide three types of computational derivatives: keypoint estimates using MediaPipe, handshape estimates using HaMeR, and 3D body reconstructions using SAM 3D Body. Signs are aligned with lexical entries in the NGT Signbank to provide interoperability of the database with other NGT resources. We outline the construction of the dataset and provide information on opportunities for reuse, for example in the context of psycholinguistic studies or in the context of sign language technology. All materials are available for non-commercial reuse under a CC BY-NC 4.0 license.} } @inproceedings{esselink:24042:sign-lang:lrec, author = {Esselink, Lyke and Oomen, Marloes and Roelofsen, Floris}, title = {Evaluating Inter-Annotator Agreement for Non-Manual Markers in Sign Languages}, pages = {66--76}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24042.html}, abstract = {This paper is part of a larger project that aims to create a standardized procedure for annotating non-manual markers (NMMs) in sign language data. The paper describes two approaches to evaluating inter-annotator agreement, the event-based approach and the frame-based approach, and uses a combination of these two approaches to evaluate the annotation guidelines introduced in Oomen et al. (2023). The evaluation reveals that for several labels in the annotation scheme inter-annotator agreement is rather low. This indicates that the annotations guidelines need to be further improved. We present concrete recommendations for how this may be achieved, and intend to implement these recommendations in future work. All data and analysis scripts are available.} } @inproceedings{klomp:24036:sign-lang:lrec, author = {Klomp, Ulrika and Gierman, Lisa and Manders, Pieter and Nauta, Ellen Yassine and Otterspeer, Gom{\`e}r and Pelupessy, Ray and Stern, Galya and Venter, Dalene and Wubbolts, Casper and Oomen, Marloes and Roelofsen, Floris}, title = {An Extension of the {NGT} Dataset in {Global} {Signbank}}, pages = {178--183}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24036.html}, abstract = {To support language documentation, linguistic research, and acquisition of Sign Language of the Netherlands (NGT), we are expanding the NGT dataset in the lexical database Global Signbank. Our most prioritized goal is to add ca. 11,000 glosses (entries). We further aim at adding ca. 3,000 example sentences and to provide linguistic information with as many glosses as possible. As for linguistic information, Signbank allows for extensive phonological descriptions of signs, and the addition of multiple senses per sign, which we would like to connect to synsets in the Multilingual Sign Language Wordnet. Additionally, we are recording extra video data: we make multiple videos of the same sign, taken from different angles, and videos with non-manual expressions. Furthermore, we are collecting motion capture data, for improved (automatic) sign language recognition and production in the future. In this paper, we describe how we proceed, the decisions that have been made so far, and future uses of the dataset.} } @inproceedings{otterspeer:24044:sign-lang:lrec, author = {Otterspeer, Gom{\`e}r and Klomp, Ulrika and Roelofsen, Floris}, title = {{SignCollect}: A `Touchless' Pipeline for Constructing Large-scale Sign Language Repositories}, pages = {269--275}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24044.html}, abstract = {The projectteam of the Signbank project at the University of Amsterdam intends to substantially extend the NGT lexicon in Global Signbank within a limited timespan. To make this possible, the signCollect platform was developed to automate a major part of the workflow. The signCollect system includes a `touchless' interface which enables a signer to control the system through simple gestures (recognized using computer vision) to (i) prompt the display of the next gloss, (ii) start a new recording, and (iii) approve/disapprove a recording. This capability allows a signer to record between 60 to 120 signs per hour, without the need for any assisting staff to be present. The approved recordings immediately become visible in the signCollect database, so that other members of the team can add metadata. With feedback from workshop participants we intend to further optimize the signCollect platform and make it available as an open-source tool for all sign language research teams.} } @inproceedings{ranum:24030:sign-lang:lrec, author = {Ranum, Oline and Otterspeer, Gom{\`e}r and Andersen, Jari I. and Belleman, Robert G. and Roelofsen, Floris}, title = {{3D-LEX} v1.0 -- {3D} Lexicons for {American} {Sign} {Language} and {Sign} {Language} of the {Netherlands}}, pages = {290--301}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Mesch, Johanna and Schulder, Marc}, booktitle = {Proceedings of the {LREC-COLING} 2024 11th Workshop on the Representation and Processing of Sign Languages: Evaluation of Sign Language Resources}, maintitle = {2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation ({LREC-COLING} 2024)}, publisher = {{ELRA Language Resources Association (ELRA) and the International Committee on Computational Linguistics (ICCL)}}, address = {Torino, Italy}, day = {25}, month = may, year = {2024}, isbn = {978-2-493814-30-2}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/24030.html}, abstract = {In this work, we present an efficient approach for capturing sign language in 3D, introduce the 3D-LEX v1.0 dataset, and detail a method for semi-automatic annotation of phonetic properties. Our procedure integrates three motion capture techniques encompassing high-resolution 3D poses, 3D handshapes, and depth-aware facial features, to attain an average sampling rate of one sign every 10 seconds. This includes the time for presenting a sign example, performing and recording the sign, and archiving the capture. The 3D-LEX dataset includes 1,000 signs from American Sign Language and an additional 1,000 signs from the Sign Language of the Netherlands. We showcase the dataset utility by presenting a simple method for generating handshape annotations directly from 3D-LEX. We produce handshape labels for 1,000 signs from American Sign Language and evaluate the labels in a sign recognition task. The labels enhance gloss recognition accuracy by 5{\%} over using no handshape annotations, and by 1{\%} over expert annotations. Our motion capture data supports in-depth analysis of sign features and facilitates the generation of 2D projections from any viewpoint. The 3D-LEX collection has been aligned with existing sign language benchmarks and linguistic resources, to support studies in 3D-aware sign language processing.} } @inproceedings{vangemert:70017:sltat:lrec, author = {Van Gemert, Britt and Cokart, Richard and Esselink, Lyke and De Meulder, Maartje and Sijm, Nienke and Roelofsen, Floris}, title = {First Steps Towards a Signing Avatar for Railway Travel Announcements in the {Netherlands}}, pages = {109--116}, editor = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and McDonald, John C. and Shterionov, Dimitar and Wolfe, Rosalee}, booktitle = {Proceedings of the 7th International Workshop on Sign Language Translation and Avatar Technology: The Junction of the Visual and the Textual: Challenges and Perspectives}, maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)}, publisher = {{European Language Resources Association (ELRA)}}, address = {Marseille, France}, day = {24}, month = jun, year = {2022}, isbn = {979-10-95546-82-5}, language = {english}, url = {https://www.sign-lang.uni-hamburg.de/lrec/pub/2022.sltat-1.17.html}, abstract = {This paper presents first steps towards a sign language avatar for communicating railway travel announcements in Dutch Sign Language. Taking an interdisciplinary approach, it demonstrates effective ways to employ co-design and focus group methods in the context of developing sign language technology, and presents several concrete findings and results obtained through co-design and focus group sessions which have not only led to improvements of our own prototype but may also inform the development of signing avatars for other languages and in other application domains.} }