@inproceedings{vazquezenriquez:24033:sign-lang:lrec,
  author    = {V{\'a}zquez-Enr{\'i}quez, Manuel and Alba-Castro, Jos{\'e} Luis and P{\'e}rez-P{\'e}rez, Ania and Cabeza-Pereiro, Mar{\'i}a del Carmen and Doc{\'i}o-Fern{\'a}ndez, Laura},
  title     = {{SignaMed}: a Cooperative Bilingual {LSE-Spanish} Dictionary in the Healthcare Domain},
  pages     = {386--394},
  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/24033.html},
  abstract  = {In this paper we present SignaMed, a bilingual dictionary accessible in Spanish and LSE (Spanish Sign Language) specific to the medical domain. Building a sign language dataset to develop machine learning algorithms and linguistic studies is a complex task that requires the cooperation of Deaf people. The dictionary platform, built with their contributions, offers diverse access modes for users, including basic search functionalities, games, and activities for sign donation. It allows sign searching using webcam or mobile phone capturing, facilitating intuitive interaction and feedback. The article presents the technical, linguistic and cooperation details behind the construction of the dictionary and will hopefully serve as inspiration for similar initiatives in other sign languages. The dictionary is accessible through https://signamed.web.app.}
}

@inproceedings{dociofernandez:20013:sign-lang:lrec,
  author    = {Doc{\'i}o-Fern{\'a}ndez, Laura and Alba-Castro, Jos{\'e} Luis and Torres-Guijarro, Soledad and Rodr{\'i}guez-Banga, Eduardo and Rey-Area, Manuel and P{\'e}rez-P{\'e}rez, Ania and Rico-Alonso, Sonia and Garc{\'i}a-Mateo, Carmen},
  title     = {{LSE{\_}UVIGO}: A Multi-source Database for {Spanish} {Sign} {Language} Recognition},
  pages     = {45--52},
  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/20013.html},
  abstract  = {This paper presents LSE{\_}UVIGO, a multi-source database designed to foster research on Sign Language Recognition. It is being recorded and compiled for Spanish Sign Language (LSE acronym in Spanish) and contains also spoken Galician language, so it is very well fitted to research on these languages, but also quite useful for fundamental research in any other sign language. LSE{\_}UVIGO is composed of two datasets: LSE{\_}Lex40{\_}UVIGO, a multi-sensor and multi-signer dataset acquired from scratch, designed as an incremental dataset, both in complexity of the visual content and in the variety of signers. It contains static and co-articulated sign recordings, fingerspelled and gloss-based isolated words, and sentences. Its acquisition is done in a controlled lab environment in order to obtain good quality videos with sharp video frames and RGB and depth information, making them suitable to try different approaches to automatic recognition. The second subset, LSE{\_}TVGWeather{\_}UVIGO is being populated from the regional television weather forecasts interpreted to LSE, as a faster way to acquire high quality, continuous LSE recordings with a domain-restricted vocabulary and with a correspondence to spoken sentences.}
}