@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",
booktitle = "Proceedings of the 12th 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",
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/",
language = "English",
ISBN = "979-10-95546-34-4",
}
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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
%S Proceedings of the 12th Language Resources and Evaluation Conference
%D 2020
%8 may
%I European Language Resources Association
%C Marseille, France
%@ 979-10-95546-34-4
%G English
%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