@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://aclanthology.org/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.",
}
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%0 Conference Proceedings
%T MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set
%A Jedlička, Pavel
%A Krňoul, Zdeněk
%A Zelezny, Milos
%A Muller, Ludek
%Y Efthimiou, Eleni
%Y Fotinea, Stavroula-Evita
%Y Hanke, Thomas
%Y Hochgesang, Julie A.
%Y Kristoffersen, Jette
%Y Mesch, Johanna
%Y Schulder, Marc
%S Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources
%D 2022
%8 June
%I European Language Resources Association
%C Marseille, France
%F jedlicka-etal-2022-mc
%X 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.
%U https://aclanthology.org/2022.signlang-1.14
%P 88-93
Markdown (Informal)
[MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set](https://aclanthology.org/2022.signlang-1.14) (Jedlička et al., SignLang 2022)
ACL
- Pavel Jedlička, Zdeněk Krňoul, Milos Zelezny, and Ludek Muller. 2022. MC-TRISLAN: A Large 3D Motion Capture Sign Language Data-set. In Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources, pages 88–93, Marseille, France. European Language Resources Association.