When Sign Language Meets Semantic Communications

Vasileios Kouvakis, Stylianos E. Trevlakis, Alexandros Apostolos A. Boulogeorgos, Theodoros Tsiftsis, Keshav Singh, Nan Qi

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

Abstract

This paper presents an American sign language (ASL) semantic communications scheme. The scheme consists of a semantic encoder that leverages a convolutional neural network to effectively utilize the ASL alphabet. The encoded information is transmitted with the 24-QAM quadrature amplitude modulation (QAM). Additionally, this paper introduces a dataset that involves the overlaying of red-green-blue landmarks and key-points onto the acquired images, thereby augmenting the depiction of hand posture. The quantification of the proposed system's training, testing, and communication performance is accomplished through numerical results, which serve to emphasize the attainable benefits and stimulate meaningful discussions.

Original languageEnglish
Title of host publication2024 IEEE 35th International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350362244
DOIs
Publication statusPublished - 2024
Externally publishedYes
Event35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024 - Valencia, Spain
Duration: 2 Sept 20245 Sept 2024

Publication series

NameIEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC
ISSN (Print)2166-9570
ISSN (Electronic)2166-9589

Conference

Conference35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024
Country/TerritorySpain
CityValencia
Period2/09/245/09/24

Keywords

  • American sign language
  • convolutional neural network
  • quadrature amplitude modulation
  • semantic communications

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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