@inproceedings{mukushev-etal-2020-evaluation,
title = "Evaluation of Manual and Non-manual Components for Sign Language Recognition",
author = "Mukushev, Medet and
Sabyrov, Arman and
Imashev, Alfarabi and
Koishybay, Kenessary and
Kimmelman, Vadim and
Sandygulova, Anara",
editor = "Calzolari, Nicoletta and
B{\'e}chet, Fr{\'e}d{\'e}ric and
Blache, Philippe and
Choukri, Khalid and
Cieri, Christopher and
Declerck, Thierry and
Goggi, Sara and
Isahara, Hitoshi and
Maegaard, Bente and
Mariani, Joseph and
Mazo, H{\'e}l{\`e}ne and
Moreno, Asuncion and
Odijk, Jan and
Piperidis, Stelios",
booktitle = "Proceedings of the Twelfth Language Resources and Evaluation Conference",
month = may,
year = "2020",
address = "Marseille, France",
publisher = "European Language Resources Association",
url = "https://aclanthology.org/2020.lrec-1.745/",
pages = "6073--6078",
language = "eng",
ISBN = "979-10-95546-34-4",
abstract = "The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign`s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2{\%} of accuracy for dataset with 20 signs and 77.9{\%} of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: \url{https://krslproject.github.io/krsl20/}"
}
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<abstract>The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign‘s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/</abstract>
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%0 Conference Proceedings
%T Evaluation of Manual and Non-manual Components for Sign Language Recognition
%A Mukushev, Medet
%A Sabyrov, Arman
%A Imashev, Alfarabi
%A Koishybay, Kenessary
%A Kimmelman, Vadim
%A Sandygulova, Anara
%Y Calzolari, Nicoletta
%Y Béchet, Frédéric
%Y Blache, Philippe
%Y Choukri, Khalid
%Y Cieri, Christopher
%Y Declerck, Thierry
%Y Goggi, Sara
%Y Isahara, Hitoshi
%Y Maegaard, Bente
%Y Mariani, Joseph
%Y Mazo, Hélène
%Y Moreno, Asuncion
%Y Odijk, Jan
%Y Piperidis, Stelios
%S Proceedings of the Twelfth Language Resources and Evaluation Conference
%D 2020
%8 May
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G eng
%F mukushev-etal-2020-evaluation
%X The motivation behind this work lies in the need to differentiate between similar signs that differ in non-manual components present in any sign. To this end, we recorded full sentences signed by five native signers and extracted 5200 isolated sign samples of twenty frequently used signs in Kazakh-Russian Sign Language (K-RSL), which have similar manual components but differ in non-manual components (i.e. facial expressions, eyebrow height, mouth, and head orientation). We conducted a series of evaluations in order to investigate whether non-manual components would improve sign‘s recognition accuracy. Among standard machine learning approaches, Logistic Regression produced the best results, 78.2% of accuracy for dataset with 20 signs and 77.9% of accuracy for dataset with 2 classes (statement vs question). Dataset can be downloaded from the following website: https://krslproject.github.io/krsl20/
%U https://aclanthology.org/2020.lrec-1.745/
%P 6073-6078
Markdown (Informal)
[Evaluation of Manual and Non-manual Components for Sign Language Recognition](https://aclanthology.org/2020.lrec-1.745/) (Mukushev et al., LREC 2020)
ACL