@inproceedings{Papadimitriou_2023,
title = {SL-REDU GSL: A Large Greek Sign Language Recognition Corpus},
author = {Katerina Papadimitriou and Galini Sapountzaki and Kiriaki Vasilaki and Eleni Efthimiou and Stavroula-Evita Fotinea and Gerasimos Potamianos},
url = {http://dx.doi.org/10.1109/ICASSPW59220.2023.10193306},
doi = {10.1109/icasspw59220.2023.10193306},
year = {2023},
date = {2023-06-01},
urldate = {2023-06-01},
booktitle = {2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW)},
publisher = {IEEE},
abstract = {We present a large multi-signer video corpus for the Greek Sign Language (GSL), suitable for the development and evaluation of GSL recognition algorithms. The database has been collected as part of the “SL-ReDu” project that focuses on the education use-case of systematic teaching of GSL as a second language (L2). The project aims to assist this process by allowing self-monitoring and objective assessment of GSL learners’ productions through the use of recognition technology, thus requiring suitable data resources relevant to the aforementioned use-case. To this end, we present the SL-ReDu GSL corpus, an extensive RGB+D video collection of 21 informants with a duration of 36 hours, recorded under studio conditions, consisting of: (i) isolated signs; (ii) continuous signing (annotated at the sentence level); and (iii) fingerspelling of words. We provide a detailed description of the design and acquisition methods used to develop it, along with corpus statistics and a comparison to existing sign language datasets. The SL-ReDu GSL corpus, as well as proposed frameworks for recognition experiments on it, are publicly available at https://www.sl-redu.e-ce.uth.gr/corpus.},
keywords = {},
pubstate = {published},
tppubtype = {inproceedings}
}
