article · Interactive Learning Environments
A dual-translation system has been developed to support communication and learning in Arabic Sign Language (ArSL). The platform comprises two main components: a speech-to-sign module driven by a speech recognition engine, and a sign-to-speech module that converts sign images into spoken words using the GTTS library. Designed for both deaf and hearing individuals, the system features an expandable sign database and is accessible through an online web interface. During technical evaluation, the system achieved a sign language recognition accuracy rate of 99 percent. Practical experimental testing demonstrated that Arab deaf students experienced significant learning improvements when using the tool. User evaluation questionnaires further revealed that participants preferred this digital method for teaching and learning, noting successful acquisition of new concepts and indicating strong potential for academic use.
Communication barriers between deaf individuals and hearing communities often restrict educational and social opportunities. Providing an effective, two-way translation system between speech and Arabic Sign Language can enhance communication for both groups. When applied in academic settings, such tools can significantly improve learning outcomes for deaf students and offer an accessible, preferred method for teaching and mastering sign language.
The system is designed for educational and communication use by deaf students, educators, and hearing individuals learning Arabic Sign Language. Because it has been implemented as an online platform and tested experimentally with students, the technology sits at an applied and tested stage, near ready for academic pilot deployment. Its expandable database enables the addition of new vocabulary, which could support wider adoption across schools and universities.
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This paper proposes a new system to translate an Arabic Sign Language (ArSL). The system consists of two sub-systems: the first, Speech to ArSL translation Subsystem. This sub-system is mainly based on the speech recognition engine. The second is ArSL to speech translation subsystem to translate the images of signs into speech, mainly based on GTTS library. This system will be easily used by both groups (the hearing disabled and normal persons) who want to learn ArSL and will support more communication between them. Among the advantages of this system is that it is expandable by adding new signs to the database and can be accessed online at: https://sr.gravita-demo.com/. The proposed system was evaluated using several methods. The findings show that the system can translate ArSL with a recognition rate of 99%. An experimental approach was used to evaluate the effectiveness of the proposed system. Results show that the proposed system was effective and the ability of Arab deaf students to learn improved significantly. In addition, the system's performance evaluation questionnaire revealed that system users preferred this approach to ArSL learning and teaching and acquired new concepts, which predicts a promising future for this system in academic environments.
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DOI: 10.1080/10494820.2021.1920431
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