Sign Gesture to Audio Conversion

J, Rajesh K; M, Vinod; A, Pavankumar A; Mishra, Shivam

doi:https://doi.org/10.55041/ijcope.v2i4.794

Volume 02, Issue 04

Published on: April 2026

SIGN GESTURE TO AUDIO CONVERSION

Rajesh K J Vinod M Pavankumar A A Shivam Mishra

Asha S

Dept Electronics and communication

East point college of engineering and technology Bengaluru Karnataka India

DOI:https://doi.org/10.55041/ijcope.v2i4.794

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Abstract

Communication barriers between hearing- impaired individuals and the general population often limit effective interaction. Sign Gesture to Audio Conversion systems aim to bridge this gap by translating hand gestures, typically from sign language, into audible speech in real time. This project utilizes sensors such as flex sensors or computer vision techniques to detect and recognize hand gestures accurately. The captured gestures are processed using a microcontroller or machine learning algorithms to map them to corresponding words or phrases. Subsequently, a Text-to-Speech (TTS) module converts the interpreted text into audio output, enabling seamless communication. This system not only enhances social interaction for the hearing- impaired but also promotes inclusivity and accessibility in education, workplaces, and public environments. accuracy, and user-friendly

How to Cite this Paper

J, R. K., M, V., A, P. A. & Mishra, S. (2026). Sign Gesture to Audio Conversion. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.794

J, Rajesh, et al.. "Sign Gesture to Audio Conversion." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.794.

J, Rajesh,Vinod M,Pavankumar A, and Shivam Mishra. "Sign Gesture to Audio Conversion." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.794.

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References

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Deep ASL Team. (2021). Deep ASL: Enabling ubiquitous and non-intrusive word and sentence-level sign language translation. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (IMWUT), 5(1),1–26.https://doi.org/10.1145/3448115

Sign2Speech Research Group. (2024). Sign2Speech: Integrating large language models to enhance semantic translation in sign language recognition. Journal of Artificial Intelligence in Accessibility, 2(1), 45–60

Ma et (2024) Z. Ma, J. Liu, and K. Gao, “MS2SL: Multimodal sequence-to- sign language framework via diffusion models,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024.

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•Peer Review Type: Double-Blind Peer Review
•Published on: Apr 27 2026

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