Synopsis

deep learning methods have become increasingly popular for building intelligent systems. Currently, many deep learning architectures constitute the state of the art in their respective domains, such as image recognition, text generation, speech recognition, etc. The availability of mature libraries and frameworks to develop such systems is also a key factor in this success.

This work explores the use of these architectures to build intelligent systems for sign languages. The creation of large sign language data corpora has made it possible to train deep learning architectures from scratch. The contributions presented in this work cover all aspects of the development of an intelligent system based on deep learning.

A first contribution is the creation of a database for the Langue des Signes de Belgique Francophone (LSFB). This is derived from an existing corpus and has been adapted to the needs of deep learning methods. The possibility of using crowdsourcing methods to collect more data is also explored.

The second contribution is the development or adaptation of architectures for automatic sign language recognition. The use of contrastive methods to learn better representations is explored, and the transferability of these representations to other sign languages is assessed.

Finally, the last contribution is the integration of models into software for the general public. This led to a reflection on the challenges of integrating an intelligent module into the software development life cycle.

Jury members

  • Prof. Wim VANHOOF, President, University of Namur
  • Prof. Benoît FRENAY, Promoter, University of Namur
  • Prof. Anthony CLEVE, Co-promoter, University of Namur
  • Prof. Laurence MEURANT, Internal Member, University of Namur
  • Prof. Lorenzo BARALDI, External Member, University of Modena
  • Prof. Annelies BRAFFORT, External Member, University of Paris-Saclay
  • Prof. Joni DAMBRE, External Member, University of Ghent

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For a good organization, please give your answer by Friday June 6.

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