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Recommending the Workflow of Vietnamese Sign Language Translation via a Comparison of Several Classification Algorithms

  • Luyl-Da Quach
  • Nghia Duong-Trung
  • Anh-Van Vu
  • Chi-Ngon NguyenEmail author
Conference paper
  • 19 Downloads
Part of the Communications in Computer and Information Science book series (CCIS, volume 1215)

Abstract

The Vietnamese deaf community communicates via a special language called Vietnamese Sign Language (VSL). Three-dimensional space and hand gesture are primarily used to convey meanings that allow deaf people to communicate among themselves and with non-deaf people around them. It maintains syntax, grammar, and vocabulary which is completely different from regular spoken and/or written Vietnamese. The normal procedure of transformation from spoken and/or written language (SWL) to VSL consists of consecutive steps: (i) Vietnamese word tokenization, (ii) machine translation into written sign language sentences, and (iii) conversion of these written sign language sentences into visual sign gesture. In this procedure, the second step gets the most attention due to the completion of the conveyed message. The basic challenge is that sign language, in general, has limited vocabulary compared to spoken/written language. If the machine translation is poorly performed, the complete message might not be successfully communicated, or in some cases, the conveyed message has a different meaning from the original. Consequently, the high accuracy translation should be maintained in any circumstances. This research is the efforts of evaluating an effective classification algorithm that the authors recommend to be integrated into the workflow of VSL translation. We believe that this is the first showing a quantitative comparison of several classification algorithms used in the workflow. The experimental results show that the translation accuracy rate is 96.7% which strongly support our recommendation.

Keywords

Machine translation Vietnamese Sign Language Example-Based Machine Translation Linguistics 

Notes

Acknowledgment

Many thanks to the MOET’s Research Project named “A study of proposing a solution to translate TV’s News into 3D sign language animations for the deaf”, project code no.: B2013-16-31, for funding this study. The authors would like to thank the Center for Research and Education of the Deaf and Hard of Hearing for supporting this study.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.FPT UniversityCan Tho CityVietnam
  2. 2.Korea Advanced Institute of Science and TechnologyDaejeonSouth Korea
  3. 3.Can Tho UniversityCan Tho CityVietnam

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