@inproceedings{jedlicka-etal-2022-mc,
    title = "{MC}-{TRISLAN}: A Large 3{D} Motion Capture Sign Language Data-set",
    author = "Jedli{\v{c}}ka, Pavel  and
      Kr{\v{n}}oul, Zden{\v{e}}k  and
      Zelezny, Milos  and
      Muller, Ludek",
    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",
    month = jun,
    year = "2022",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://preview.aclanthology.org/add-emnlp-2024-awards/2022.signlang-1.14/",
    pages = "88--93",
    abstract = "The new 3D motion capture data corpus expands the portfolio of existing language resources by a corpus of 18 hours of Czech sign language. This helps to alleviate the current problem, which is a critical lack of high quality data necessary for research and subsequent deployment of machine learning techniques in this area. We currently provide the largest collection of annotated sign language recordings acquired by state-of-the-art 3D human body recording technology for the successful future deployment in communication technologies, especially machine translation and sign language synthesis."
}