The Acceptance of Culturally Adapted Signing Avatars Among Deaf and Hard-of-Hearing Individuals

In Turkey, around 3 million people have hearing impairments, and the shift to digital platforms during the pandemic has worsened accessibility challenges as websites and apps often ignore their needs. Research shows that 50% of hearing-impaired individuals struggle to understand written text due to Turkish Sign Language being their first language, with Turkish serving as a second language. Differences in grammar between Turkish and Turkish Sign Language, along with a limited sign language vocabulary, further hinder comprehension. To solve this, we developed AI-powered sign language translation systems that allow people who are deaf or hard of hearing to access digital content in Turkish Sign Language. SignForDeaf's system translates text into sign language videos using Natural Language Processing (NLP) and generates seamless videos with smooth word transitions. Currently, this system supports Turkish Sign Language, with future plans to include other languages like Arabic, American, British, and Finnish Sign Language. The system was designed in collaboration with sign language experts to ensure accuracy and an inclusive development of a digital environment.

Application of AI in Turkish Sign Language Translation: A Case Study of its Use and Purpose

Celik,

Reza

2024

Tunisian Sign Language Recognition System of Static Two-Handed Asymmetrical Signs using Deep Transfer Learning

Daknou,

Hermessi,

Tabbane

2024

Deaf and Hard of Hearing people use Sign Languages in the interaction among themselves and among hearing people. The automatic recognition of Static Two-Handed Asymmetrical signs is a hard operation, since it involves the implementation of complex processing system for providing image perception. In this paper, we produce a dataset of 2000 images containing 12 Two-handed Asymmetrical Tunisian Signs and utilize transfer learning for automatic recognition, achieving 98.29 % Accuracy. The simulations prove that this best Accuracy value is yielded by the Xception model when combined with the Adagrad optimizer, which indicates that our approach achieves high results despite using a small Dataset.

Automatic Gesture-Based Arabic Sign Language Recognition: A Federated Learning Approach

Alzu'bi,

Al-Hadhrami,

Albashayreh

et al. 2024

Featuring machine learning algorithms for recognizing hand gesture patterns adjusted for individuals with disabilities is an expanding trend in assisted living. This paper addresses the challenge of interpreting the semantics of image-based hand gestures by introducing a federated deep learning architecture for Arabic sign language recognition. The proposed model manages distributed learning through a client-server paradigm, wherein several edge nodes collaborate to jointly learn the discriminative features of confidential data without breaching its privacy. This model will enable more accessibility for people with deafness or impairment using image gestures. The federated learning procedure is primarily based on the ResNet32 deep backbone and federated averaging mechanism. The experimental results show the effectiveness of the proposed FL model, achieving an accuracy of 98.30% with 33 seconds on average for each client in a single training round. This demonstrates its high capabilities in recognizing Arabic sign language and improving the communication experience for people with disabilities.