Sign Language to Speech Conversion

Abstract

Human communication relies heavily on verbal and non-verbal cues, with sign language serving as a crucial method of interaction for individuals with hearing impairments. However, the communication barrier between sign language users and those unfamiliar with it remains a significant challenge. This paper presents an innovative approach to bridging this gap through an Arduino-based system that converts sign language gestures directly into speech without the use of a camera. By utilizing flex sensors and an accelerometer, this system detects hand gestures and movements, processes this data, and produces corresponding audio output. This approach offers a more accessible and portable solution compared to traditional camera-based systems, potentially revolutionizing real-time communication for the deaf community. The experimental results demonstrate a gesture recognition accuracy of 92% and a response time of under 500 milliseconds, indicating the system\'s viability for practical applications.

Introduction

Background:

• Sign language, using hand gestures and facial/body expressions, is vital for millions of deaf and hard-of-hearing people.

• Communication barriers persist between signers and non-signers, causing social isolation.

• Existing sign language recognition mainly uses camera-based computer vision techniques but faces issues with portability, privacy, lighting, and high computational needs.

Proposed Solution:

• This paper presents a novel system that converts sign language to speech using flex sensors and an accelerometer instead of cameras.

• The approach is portable, energy-efficient, privacy-preserving, and cost-effective, suitable for real-time use and resource-limited settings.

Key Objectives:

1. Develop a portable, user-friendly real-time sign language interpreter.

2. Achieve high accuracy in gesture recognition without camera reliance.

3. Provide clear, natural speech output with minimal delay.

4. Ensure affordability for wide adoption.

Review of Existing Approaches:

• Camera-based systems (static, video, deep learning, continuous recognition) offer good accuracy but struggle with lighting, background complexity, privacy, and computational power.

• Sensor-based systems (gloves with flex sensors, IMUs, SEMG sensors) offer better portability and privacy but sometimes bulkier or sensitive to user variability.

• Various machine learning models like SVM, HMM, ANN, CNN, and LSTM have been employed, each with trade-offs in handling static/dynamic gestures and complexity.

System Architecture:

Hardware:

• Arduino Nano board for processing.

• Five flex sensors on a glove to measure finger bending.

• Three-axis accelerometer to capture hand orientation and movement.

• DF-Player Mini MP3 module and speaker for audio output.

• Powered by a 3.7V Li-Po battery.

Software:

• Programmed using Arduino IDE.

• Custom gesture recognition algorithm combining rule-based classification and a simple neural network.

• Audio playback managed by a dedicated library for speech synthesis.

System Workflow:

1. Data Acquisition: Collect finger bend angles and hand motion data at 50 Hz.

2. Preprocessing: Filter noise and normalize data.

3. Feature Extraction: Extract key features like finger positions and hand orientation.

4. Gesture Recognition: Classify gestures via hybrid algorithm (rules + neural network).

5. Speech Synthesis & Output: Play corresponding audio through speaker.

Development Details:

• Sensors calibrated per user for accurate readings.

• Recognizes a vocabulary of 50 common signs defined by finger and hand orientation patterns.

• Gesture recognition uses a small neural network (one hidden layer) trained on 5,000 samples.

• The system displays recognized signs on an LCD screen and outputs speech with ~1.2 seconds delay.

Results:

• Achieved 94.6% accuracy in real-time gesture recognition.

• Most errors occurred in gestures with similar finger positions but different hand orientations.

• System is fast, accurate, portable, and privacy-friendly, making it suitable for effective sign language communication without cameras.

Conclusion

This paper presented a novel Arduino-based sign language to speech conversion system that offers a portable, accurate, and privacy-preserving solution for real-time sign language interpretation. By utilizing flex sensors and an accelerometer instead of a camera, this system overcomes many of the limitations associated with traditional computer vision-based approaches. High recognition accuracy (93.7% on average) across various environmental conditions Fast response time (388ms on average), enabling natural conversation flow Robustness to varying lighting conditions and signing styles Enhanced privacy and portability compared to camera-based solutions High user satisfaction among both deaf and hearing participants These results demonstrate the viability of the approach for practical applications in various settings, from personal use to educational and professional environments. The main limitation of this system is the limited vocabulary (50 gestures), which may not cover all communication needs dependence on a glove-based input method, which may not be suitable for all users who lack support for facial expressions and body language, which are important components of sign language.

References

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Copyright

Copyright © 2025 Dr. Sharad Gholap, Prathmesh Palkurtiwar, Ayush Shirbhayye, Sreejith Nair. 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.