@inproceedings{brown:26026:sign-lang:lrec,
  author    = {Brown, Matt and Ranum, Oline and Fish, Edward and Proctor, Heidi and Woll, Bencie and Bowden, Richard and Cormier, Kearsy},
  title     = {{SignGPT} and the {Visual} {Language} {Toolkit}},
  pages     = {51--60},
  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/26026.html},
  abstract  = {SignGPT's Visual Language Toolkit (VLTK) aims to remove fundamental barriers to large scale sign language modelling by developing data-driven, linguistically grounded methods for continuous sign language recognition. We first identify fundamental issues around the ecological validity of potential data sources (e.g. broadcast media with interpreted signing or captions, scraping of social media). We contrast these with the currently highly resource-intensive development of curated sign language corpora based on linguistic principles. The VLTK addresses this scarcity of high quality sign language data by providing semi-automated glossing and other recognition tools, driving large scale corpus expansion without sacrificing linguistic principles. Unlike prior systems that rely on sparse glossing, the project integrates dense temporal annotation, non-manual and non-lexical feature tracking, and transformer-based architectures to capture the multimodal and spatial structure of signing. By aligning machine vision innovation with linguistic insights and community-embedded evaluation, SignGPT establishes a foundation for robust and extensible sign language models.}
}

@inproceedings{dimou:26029:sign-lang:lrec,
  author    = {Dimou, Athanasia-Lida and Goulas, Theodoros and Tsatali, Marianna and Ntova, Tarsita and Hoffmann-Lamplmair, Doris and Fotinea, Stavroula-Evita and Efthimiou, Eleni and Teichmann, Birgit and Tsolaki, Magda and Atkinson, Joanna and Woll, Bencie},
  title     = {The {De-Sign} Platform: An Online Psychometric Tool for Dementia Screening of Deaf Older Adults in two Sign Languages, {GSL} and {{\"O}GS}},
  pages     = {113--119},
  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/26029.html},
  abstract  = {This article presents the De-Sign platform, a web-based psychometric tool specifically designed for screening dementia in Deaf older adults (50+) who use Austrian Sign Language and Greek Sign Language hereinafter {\"O}GS and GSL respectively. The limited access to dementia services for these populations is primarily attributed to a scarcity of healthcare professionals fluent in sign language. Hence, enhancing access to relevant diagnostic services has become a priority. Currently, there is a significant lack of screening tools specifically developed to identify early signs of dementia that are compatible with national sign languages. To address this issue, the De-Sign Erasmus+ (2022-2025) project has employed suitable psychometric instruments that are adapted to the cultural contexts and linguistic norms of Deaf communities in Austria and Greece. The only existing Cognitive Screening Test (CST) for British Sign Language (BSL), used for diagnosing dementia in Deaf older adults, was initially adapted from English by Atkinson et al. (2015). The De-Sign platform hosts a cognitive screening test in {\"O}GS and GSL. Both were linguistically and culturally adapted from the BSL-CST test, providing two web-based versions of a psychometric tool that enables dementia screening within these populations.}
}

@inproceedings{woll:22007:sign-lang:lrec,
  author    = {Woll, Bencie and Fox, Neil and Cormier, Kearsy},
  title     = {Segmentation of Signs for Research Purposes: Comparing Humans and Machines},
  pages     = {198--201},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna and Schulder, Marc},
  booktitle = {Proceedings of the {LREC2022} 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources},
  maintitle = {13th International Conference on Language Resources and Evaluation ({LREC} 2022)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {25},
  month     = jun,
  year      = {2022},
  isbn      = {979-10-95546-86-3},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/22007.html},
  abstract  = {Sign languages such as British Sign Language (BSL) are visual languages which lack standard writing systems. Annotation of sign language data, especially for the purposes of machine readability, is therefore extremely slow. Tools to help automate and thus speed up the annotation process are very much needed. Here we test the development of one such tool (VIA-SLA), which uses temporal convolutional networks (Renz et al., 2021a, b) for the purpose of segmenting continuous signing in any sign language, and is designed to integrate smoothly with ELAN, the widely used annotation software for analysis of videos of sign language. We compare automatic segmentation by machine with segmentation done by a human, both in terms of time needed and accuracy of segmentation, using samples taken from the BSL Corpus (Schembri et al., 2014). A small sample of four short video files is tested (mean duration 25 seconds). We find that mean accuracy in terms of number and location of segmentations is relatively high, at around 78{\%}. This preliminary test suggests that VIA-SLA promises to be very useful for sign linguists.}
}

@inproceedings{liang:20031:sign-lang:lrec,
  author    = {Liang, Xing and Woll, Bencie and Epaminondas, Kapetanios and Angelopoulou, Anastasia and Al-Batat, Reda},
  title     = {Machine Learning for Enhancing Dementia Screening in Ageing Deaf Signers of {British} {Sign} {Language}},
  pages     = {135--138},
  editor    = {Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Hochgesang, Julie A. and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2020} 9th Workshop on the Representation and Processing of Sign Languages: Sign Language Resources in the Service of the Language Community, Technological Challenges and Application Perspectives},
  maintitle = {12th International Conference on Language Resources and Evaluation ({LREC} 2020)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Marseille, France},
  day       = {16},
  month     = may,
  year      = {2020},
  isbn      = {979-10-95546-54-2},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/20031.html},
  abstract  = {Ageing trend in populations is correlated with increased prevalence of acquired cognitive impairments such as dementia. Although there is no cure for dementia, a timely diagnosis helps in obtaining necessary support and appropriate medication. With this in mind, researchers are working urgently to develop effective technological tools that can help doctors undertake early identification of cognitive disorder. In this paper, we introduce  an automatic dementia screening system for ageing Deaf signers of British Sign Language (BSL), using Convolutional Neural Networks (CNN), by analysing the sign space envelope and facial expression of BSL signers using normal 2D videos from BSL corpus. Our approach firstly establishes an accurate real-time hand trajectory tracking model together with a real-time landmark facial motion analysis model to identify differences in sign space envelope and facial movement as the keys to identifying language changes associated with dementia. Based on the differences in patterns obtained from facial and trajectory motion data, CNN models (ResNet50/VGG16) are fine-tuned using Keras deep learning models to incrementally identify and improve dementia recognition rates. We report the results for two methods using different modalities (sign trajectory and facial motion), together with the performance comparisons between different deep learning CNN models in ResNet50 and VGG16. The experiments show the effectiveness of our deep learning based approach in terms of sign space tracking, facial motion tracking and early stage dementia performance assessment tasks. The results are validated against cognitive assessment scores as of our ground truth data with a test set performance of 87.88{\%}. The proposed system has potential for economical, simple, flexible, and adaptable assessment of other acquired neurological impairments associated with motor changes, such as stroke and Parkinson's disease in both hearing and Deaf people.}
}

@inproceedings{cormier:12033:sign-lang:lrec,
  author    = {Cormier, Kearsy and Fenlon, Jordan and Johnston, Trevor and Rentelis, Ramas and Schembri, Adam and Rowley, Katherine and Adam, Robert and Woll, Bencie},
  title     = {From Corpus to Lexical Database to Online Dictionary: Issues in annotation of the {BSL} Corpus and the Development of {BSL} {SignBank}},
  pages     = {7--12},
  editor    = {Crasborn, Onno and Efthimiou, Eleni and Fotinea, Stavroula-Evita and Hanke, Thomas and Kristoffersen, Jette and Mesch, Johanna},
  booktitle = {Proceedings of the {LREC2012} 5th Workshop on the Representation and Processing of Sign Languages: Interactions between Corpus and Lexicon},
  maintitle = {8th International Conference on Language Resources and Evaluation ({LREC} 2012)},
  publisher = {{European Language Resources Association (ELRA)}},
  address   = {Istanbul, Turkey},
  day       = {27},
  month     = may,
  year      = {2012},
  language  = {english},
  url       = {https://www.sign-lang.uni-hamburg.de/lrec/pub/12033.html},
  abstract  = {One requirement of a sign language corpus is that it should be machine-readable, but only a systematic approach to annotation that involves lemmatisation of the sign language glosses can make this possible at the present time. Such lemmatisation involves grouping morphological and phonological variants together into a single lemma, so that all related variants of a sign can be identified and analysed as a single sign. This lemmatisation process is made more straightforward by the existence of a comprehensive lexical database, as in the case for Australian Sign Language (Auslan). When annotation of data collected as part of the British Sign Language (BSL) Corpus Project began, no such lexical database for BSL existed. Therefore, a lemmatised BSL lexical database was created concurrently during annotation of the BSL Corpus data. As part of ongoing work by the Deafness Cognition {\&} Language Research Centre, this lexical database is being developed into an online BSL dictionary, BSL SignBank. This paper describes the adaptation of the Auslan lexical database into a BSL lexical database, and the current development of this lexical database into BSL SignBank.}
}