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Trends and challenges for sign language recognition with machine learning

J. Fink, Mathieu De Coster (UGent) , Joni Dambre (UGent) and B. Frénay
(2023) ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings. p.561-570
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Abstract
Research in natural language processing has led to the creation of powerful tools for individuals, companies... However, these successes for written languages have not yet affected signed languages (SLs) to the same extent. The creation of similar tools for signed languages would benefit deaf, hard of hearing, and hearing people by making SL content, learning, and communication more accessible for everyone. SL recognition and translation are related to AI, but require collaboration with linguists and stakeholders. This paper describes related challenges from an AI researcher’s point of view and summarizes the state of the art in these domains

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Citation

Please use this url to cite or link to this publication:

MLA
Fink, J., et al. “Trends and Challenges for Sign Language Recognition with Machine Learning.” ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings, 2023, pp. 561–70, doi:10.14428/esann/2023.ES2023-7.
APA
Fink, J., De Coster, M., Dambre, J., & Frénay, B. (2023). Trends and challenges for sign language recognition with machine learning. ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings, 561–570. https://doi.org/10.14428/esann/2023.ES2023-7
Chicago author-date
Fink, J., Mathieu De Coster, Joni Dambre, and B. Frénay. 2023. “Trends and Challenges for Sign Language Recognition with Machine Learning.” In ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings, 561–70. https://doi.org/10.14428/esann/2023.ES2023-7.
Chicago author-date (all authors)
Fink, J., Mathieu De Coster, Joni Dambre, and B. Frénay. 2023. “Trends and Challenges for Sign Language Recognition with Machine Learning.” In ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings, 561–570. doi:10.14428/esann/2023.ES2023-7.
Vancouver
1.
Fink J, De Coster M, Dambre J, Frénay B. Trends and challenges for sign language recognition with machine learning. In: ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings. 2023. p. 561–70.
IEEE
[1]
J. Fink, M. De Coster, J. Dambre, and B. Frénay, “Trends and challenges for sign language recognition with machine learning,” in ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings, Bruges, Belgium, 2023, pp. 561–570.
@inproceedings{01HBGB4KXEX308AQEYWH79CAGC,
  abstract     = {{Research in natural language processing has led to the creation of powerful tools for individuals, companies... However, these successes for written languages have not yet affected signed languages (SLs)
to the same extent. The creation of similar tools for signed languages
would benefit deaf, hard of hearing, and hearing people by making SL
content, learning, and communication more accessible for everyone. SL
recognition and translation are related to AI, but require collaboration
with linguists and stakeholders. This paper describes related challenges
from an AI researcher’s point of view and summarizes the state of the art
in these domains}},
  author       = {{Fink, J. and De Coster, Mathieu and Dambre, Joni and Frénay, B.}},
  booktitle    = {{ESANN 2023, 31st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Proceedings}},
  isbn         = {{9782875870889}},
  language     = {{eng}},
  location     = {{Bruges, Belgium}},
  pages        = {{561--570}},
  title        = {{Trends and challenges for sign language recognition with machine learning}},
  url          = {{http://doi.org/10.14428/esann/2023.ES2023-7}},
  year         = {{2023}},
}
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