Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50

M. Mukushev; A. Kydyrbekova; A. Imashev; V. Kimmelman; A. Sandygulova

Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50

M. Mukushev
, A. Kydyrbekova
, A. Imashev
, V. Kimmelman
, A. Sandygulova

Nazarbayev University

Nazarbayev University
University of Bergen

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

1 Citation (Scopus)

Abstract

This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/.

Original language	English
Title of host publication	2022 Language Resources and Evaluation Conference, LREC 2022
Editors	Nicoletta Calzolari, Frederic Bechet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Helene Mazo, Jan Odijk, Stelios Piperidis
Publisher	European Language Resources Association (ELRA)
Pages	2541-2547
Number of pages	7
ISBN (Electronic)	9791095546726
Publication status	Published - 2022
Event	13th International Conference on Language Resources and Evaluation Conference, LREC 2022 - Marseille, France Duration: Jun 20 2022 → Jun 25 2022

Publication series

Name	2022 Language Resources and Evaluation Conference, LREC 2022

Conference

Conference	13th International Conference on Language Resources and Evaluation Conference, LREC 2022
Country/Territory	France
City	Marseille
Period	6/20/22 → 6/25/22

Funding

We would like to thank the dataset contributors for agreeing to participate in data collection. This work was supported by the Nazarbayev University Faculty Development Competitive Research Grant Program 2019-2021 “Kazakh Sign Language Automatic Recognition System (K-SLARS)”. Award number is 110119FD4545.

Keywords

crowdsourcing
dataset
deaf
sign language processing

ASJC Scopus subject areas

Language and Linguistics
Library and Information Sciences
Linguistics and Language
Education

Cite this

Mukushev, M., Kydyrbekova, A., Imashev, A., Kimmelman, V., & Sandygulova, A. (2022). Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50. In N. Calzolari, F. Bechet, P. Blache, K. Choukri, C. Cieri, T. Declerck, S. Goggi, H. Isahara, B. Maegaard, J. Mariani, H. Mazo, J. Odijk, & S. Piperidis (Eds.), 2022 Language Resources and Evaluation Conference, LREC 2022 (pp. 2541-2547). (2022 Language Resources and Evaluation Conference, LREC 2022). European Language Resources Association (ELRA).

Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50. / Mukushev, M.; Kydyrbekova, A.; Imashev, A. et al.
2022 Language Resources and Evaluation Conference, LREC 2022. ed. / Nicoletta Calzolari; Frederic Bechet; Philippe Blache; Khalid Choukri; Christopher Cieri; Thierry Declerck; Sara Goggi; Hitoshi Isahara; Bente Maegaard; Joseph Mariani; Helene Mazo; Jan Odijk; Stelios Piperidis. European Language Resources Association (ELRA), 2022. p. 2541-2547 (2022 Language Resources and Evaluation Conference, LREC 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Mukushev, M, Kydyrbekova, A, Imashev, A, Kimmelman, V & Sandygulova, A 2022, Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50. in N Calzolari, F Bechet, P Blache, K Choukri, C Cieri, T Declerck, S Goggi, H Isahara, B Maegaard, J Mariani, H Mazo, J Odijk & S Piperidis (eds), 2022 Language Resources and Evaluation Conference, LREC 2022. 2022 Language Resources and Evaluation Conference, LREC 2022, European Language Resources Association (ELRA), pp. 2541-2547, 13th International Conference on Language Resources and Evaluation Conference, LREC 2022, Marseille, France, 6/20/22.

Mukushev M, Kydyrbekova A, Imashev A, Kimmelman V, Sandygulova A. Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50. In Calzolari N, Bechet F, Blache P, Choukri K, Cieri C, Declerck T, Goggi S, Isahara H, Maegaard B, Mariani J, Mazo H, Odijk J, Piperidis S, editors, 2022 Language Resources and Evaluation Conference, LREC 2022. European Language Resources Association (ELRA). 2022. p. 2541-2547. (2022 Language Resources and Evaluation Conference, LREC 2022).

Mukushev, M. ; Kydyrbekova, A. ; Imashev, A. et al. / Crowdsourcing Kazakh-Russian Sign Language : FluentSigners-50. 2022 Language Resources and Evaluation Conference, LREC 2022. editor / Nicoletta Calzolari ; Frederic Bechet ; Philippe Blache ; Khalid Choukri ; Christopher Cieri ; Thierry Declerck ; Sara Goggi ; Hitoshi Isahara ; Bente Maegaard ; Joseph Mariani ; Helene Mazo ; Jan Odijk ; Stelios Piperidis. European Language Resources Association (ELRA), 2022. pp. 2541-2547 (2022 Language Resources and Evaluation Conference, LREC 2022).

@inproceedings{bbadd03eb88d473084c307f34cf24bf9,

title = "Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50",

abstract = "This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/.",

keywords = "crowdsourcing, dataset, deaf, sign language processing",

author = "M. Mukushev and A. Kydyrbekova and A. Imashev and V. Kimmelman and A. Sandygulova",

note = "Funding Information: We would like to thank the dataset contributors for agreeing to participate in data collection. This work was supported by the Nazarbayev University Faculty Development Competitive Research Grant Program 2019-2021 {\textquotedblleft}Kazakh Sign Language Automatic Recognition System (K-SLARS){\textquotedblright}. Award number is 110119FD4545. Publisher Copyright: {\textcopyright} European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.; 13th International Conference on Language Resources and Evaluation Conference, LREC 2022 ; Conference date: 20-06-2022 Through 25-06-2022",

year = "2022",

language = "English",

series = "2022 Language Resources and Evaluation Conference, LREC 2022",

publisher = "European Language Resources Association (ELRA)",

pages = "2541--2547",

editor = "Nicoletta Calzolari and Frederic Bechet and Philippe Blache and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Helene Mazo and Jan Odijk and Stelios Piperidis",

booktitle = "2022 Language Resources and Evaluation Conference, LREC 2022",

}

TY - GEN

T1 - Crowdsourcing Kazakh-Russian Sign Language

T2 - 13th International Conference on Language Resources and Evaluation Conference, LREC 2022

AU - Mukushev, M.

AU - Kydyrbekova, A.

AU - Imashev, A.

AU - Kimmelman, V.

AU - Sandygulova, A.

N1 - Funding Information: We would like to thank the dataset contributors for agreeing to participate in data collection. This work was supported by the Nazarbayev University Faculty Development Competitive Research Grant Program 2019-2021 “Kazakh Sign Language Automatic Recognition System (K-SLARS)”. Award number is 110119FD4545. Publisher Copyright: © European Language Resources Association (ELRA), licensed under CC-BY-NC-4.0.

PY - 2022

Y1 - 2022

N2 - This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/.

AB - This paper presents the methodology we used to crowdsource a data collection of a new large-scale signer independent dataset for Kazakh-Russian Sign Language (KRSL) created for Sign Language Processing. By involving the Deaf community throughout the research process, we firstly designed a research protocol and then performed an efficient crowdsourcing campaign that resulted in a new FluentSigners-50 dataset. The FluentSigners-50 dataset consists of 173 sentences performed by 50 KRSL signers for 43,250 video samples. Dataset contributors recorded videos in real-life settings on various backgrounds using various devices such as smartphones and web cameras. Therefore, each dataset contribution has a varying distance to the camera, camera angles and aspect ratio, video quality, and frame rates. Additionally, the proposed dataset contains a high degree of linguistic and inter-signer variability and thus is a better training set for recognizing a real-life signed speech. FluentSigners-50 is publicly available at https://krslproject.github.io/fluentsigners-50/.

KW - crowdsourcing

KW - dataset

KW - deaf

KW - sign language processing

UR - https://www.scopus.com/pages/publications/85144466843

UR - https://www.scopus.com/pages/publications/85144466843#tab=citedBy

M3 - Conference contribution

AN - SCOPUS:85144466843

T3 - 2022 Language Resources and Evaluation Conference, LREC 2022

SP - 2541

EP - 2547

BT - 2022 Language Resources and Evaluation Conference, LREC 2022

A2 - Calzolari, Nicoletta

A2 - Bechet, Frederic

A2 - Blache, Philippe

A2 - Choukri, Khalid

A2 - Cieri, Christopher

A2 - Declerck, Thierry

A2 - Goggi, Sara

A2 - Isahara, Hitoshi

A2 - Maegaard, Bente

A2 - Mariani, Joseph

A2 - Mazo, Helene

A2 - Odijk, Jan

A2 - Piperidis, Stelios

PB - European Language Resources Association (ELRA)

Y2 - 20 June 2022 through 25 June 2022

ER -

Crowdsourcing Kazakh-Russian Sign Language: FluentSigners-50

Abstract

Publication series

Conference

Funding

Keywords

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this