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Vol.14, No.4, November 2025. ISSN: 2217-8309 eISSN: 2217-8333
TEM Journal
TECHNOLOGY, EDUCATION, MANAGEMENT, INFORMATICS Association for Information Communication Technology Education and Science |
Development of a Speech-to-Sign Language Translation System Using Machine Learning and Computer Vision: A Bulgarian Case Study
Simeon Nikolov, George Pashev, Silvia Gaftandzhieva
© 2025 Silvia Gaftandzhieva, published by UIKTEN. This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. (CC BY-NC-ND 4.0)
Citation Information: TEM Journal. Volume 14, Issue 4, Pages 3227-3241, ISSN 2217-8309, DOI: 10.18421/TEM144-31, November 2025.
Received: 06 January 2025.
Abstract:
While sign language is the primary communication tool for deaf individuals, the limited number of sign language interpreters creates substantial obstacles to social integration and access to essential services. The paper presents the development of an automated translation system that converts spoken Bulgarian language into animated Bulgarian Sign Language (BgSL) using machine learning and computer vision techniques. The system addresses the communication barrier between hearing and deaf individuals by providing real-time translation capabilities. The proposed solution combines speech recognition, natural language processing, and video-based gesture visualization to create an accessible communication tool. The system achieves 90% accuracy in speech recognition and 78% accuracy in text-to-gesture correspondence, demonstrating its potential for practical applications. The paper discusses future improvements and potential applications in education.
Keywords – Sign language translation, machine learning, computer vision, speech recognition, assistive technology, natural language processing. |
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