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Deep Learning and Sign Language Models Based Enhanced Accessibility of e-governance Services for Speech and Hearing-Impaired

  • R. Jennifer Eunice
  • , D. Jude Hemanth*
  • *Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Sign Language is the basic building block to communicate with the hearing and speech impaired. This can be made easy by developing a robust system to transcribe real-life spoken language sentences into its sign language sequence video and contra wise. Such a system is built with both a sign recognition unit and a sign translation unit. In this paper we provide an in-depth analysis of the existing proposed models to develop such a robust system, discussing their pros and cons. In addition to that, we evaluate the performance of those models based on the quality outcome from the video generation unit. We also brief the future scope in establishing real-life SLP communication models build with advanced deep learning architectures for the hearing and speech disabled thus paving the way to impart education and employment among the hearing and speech impaired.

Original languageEnglish
Title of host publicationElectronic Governance with Emerging Technologies - 1st International Conference, EGETC 2022, Revised Selected Papers
EditorsFernando Ortiz-Rodríguez, Sanju Tiwari, Miguel-Angel Sicilia, Anastasija Nikiforova
PublisherSpringer Science and Business Media Deutschland GmbH
Pages12-24
Number of pages13
ISBN (Print)9783031229497
DOIs
Publication statusPublished - 2022
Event1st International Conference on Electronic Governance with Emerging Technologies, EGETC 2022 - Tampico, Mexico
Duration: 12-09-202214-09-2022

Publication series

NameCommunications in Computer and Information Science
Volume1666 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference1st International Conference on Electronic Governance with Emerging Technologies, EGETC 2022
Country/TerritoryMexico
CityTampico
Period12-09-2214-09-22

All Science Journal Classification (ASJC) codes

  • General Computer Science
  • General Mathematics

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