ASL Voice Bridge: American Sign Language to Speech Converter Using Raspberry PI

Abstract

Communication is essential for human interaction, but for deaf and mute individuals, it remains a major challenge. Sign language is their primary mode of communication, yet most people are not trained to understand it. This gap creates barriers that often lead to social isolation and dependence on interpreters. Therefore, there is a need for an automated, reliable, and affordable solution to bridge this communication gap. The project “ASL Voice Bridge: American Sign Language to Speech Converter Using Raspberry Pi” presents an assistive technology that translates ASL hand gestures into speech in real time. The system uses a camera module connected to a Raspberry Pi to capture hand gestures. Computer vision techniques detect and track hand movements, while machine learning algorithms classify gestures based on extracted features such as finger positions and palm orientation. Once a gesture is recognized, it is converted into text and then into speech using a text-to-speech engine, enabling immediate communication. This vision-based system eliminates the need for wearable devices, making it more user-friendly and practical. Overall, the project improves accessibility and inclusivity, with future scope for deep learning integration, multilingual support, and enhanced real-world applications.

Introduction

The paper presents “ASL Voice Bridge,” a Raspberry Pi–based system that translates American Sign Language (ASL) gestures into speech in real time. Using a camera module, the system captures hand gestures, which are processed via image preprocessing and a trained ONNX machine learning model (CNN) to recognize the corresponding ASL alphabet. Recognized gestures are then converted into text and speech using a text-to-speech (TTS) engine, output through a speaker, providing a portable, affordable, and real-time communication tool for deaf and mute individuals.

Key features include:

• Vision-based gesture recognition eliminating the need for wearable devices.
• Real-time translation with minimal delay for practical interaction.
• Embedded system implementation using Raspberry Pi Zero 2 W for portability and cost-effectiveness.
• Audio feedback via USB sound card and amplifier for intelligible speech output.

The system was tested successfully under controlled conditions, showing satisfactory accuracy and usability, though performance can drop under poor lighting, complex backgrounds, or unusual hand positions. Future improvements may include dynamic gesture support, enhanced accuracy, and robustness in diverse environments, making it a promising tool for bridging communication gaps.

Conclusion

The project “ASL Voice Bridge” successfully demonstrates a practical and efficient solution for bridging the communication gap between deaf and mute individuals and the general population. By utilizing image processing, machine learning techniques, and the Raspberry Pi platform, the system is able to recognize American Sign Language (ASL) gestures in real time and convert them into both text and speech output. The proposed system offers a vision-based, user-friendly, and cost-effective approach, eliminating the need for wearable devices such as sensor-based gloves. The implementation proves that Raspberry Pi can effectively handle real-time gesture recognition tasks when combined with optimized algorithms. The system provides satisfactory accuracy and response time, making it suitable for basic communication in everyday environments. Although certain challenges, such as sensitivity to lighting conditions, background noise, and limited gesture datasets, were observed, the overall performance of the system is promising. These limitations can be addressed through future enhancements, including the use of advanced deep learning models, larger datasets, and improved preprocessing techniques. In conclusion, the ASL Voice Bridge system contributes to creating a more inclusive and accessible society by enabling seamless communication for deaf and mute individuals. With further improvements and scalability, the system has the potential to be widely adopted in areas such as education, healthcare, and public services, enhancing independence and quality of life for its users.

References

[1] R. Rastgoo, K. Kiani and S. Escalera, “Sign Language Recognition: A Deep Survey,” Expert Systems with Applications, vol. 164, pp. 113794, Feb. 2021. [2] O. Koller, H. Ney and R. Bowden, “Deep Learning of Mouth Shapes for Sign Language,” in Proc. IEEE International Conference on Computer Vision Workshops, Santiago, Chile, 2015, pp. 85–91. [3] A. Pigou, S. Dieleman, P. Kindermans and B. Schrauwen, “Sign Language Recognition Using Convolutional Neural Networks,” in European Conference on Computer Vision Workshops, Zurich, Switzerland, 2014, pp. 572–578. [4] G. Bradski, “The OpenCV Library,” Dr. Dobb’s Journal of Software Tools, vol. 25, no. 11, pp. 120–126, 2000. [5] Raspberry Pi Foundation, “Raspberry Pi Zero 2 W Documentation,” Available: https://www.raspberrypi.com/documentation/ [6] OpenCV Organization, “Open Source Computer Vision Library,” Available: https://opencv.org/ [7] Roboflow Inc., “Roboflow Computer Vision Platform Documentation,” Available: https://docs.roboflow.com/ [8] Microsoft, “ONNX Runtime: Open Neural Network Exchange,” Available: https://onnxruntime.ai/ [9] J. D. Allen, “eSpeak Text-to-Speech Synthesizer,” Available: http://espeak.sourceforge.net/

Copyright

Copyright © 2026 Mr. Choudhari A.V., Gaikwad Ruturaj, Rathod Nandini, Hippragi Nikita, Mane Sarthak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.