Speech-to-Text Conversion for Accessibility Tools

Introduction

Speech-to-text conversion is a critical technology in enhancing accessibility for individuals with disabilities. This project proposal outlines a system designed to convert spoken language into text, facilitating easier interaction with digital interfaces for users with physical and sensory impairments. The project draws inspiration from recent advancements in automatic speech recognition (ASR) systems, which have highlighted the potential of these technologies to improve inclusivity and accessibility.

Background

Recent research has demonstrated that ASR systems can significantly aid individuals with disabilities by converting human speech into machine-readable text. These systems are particularly beneficial for those with limited mobility or visual impairments, enabling them to interact with digital devices more naturally and efficiently. Advances in speech technology have made these systems more accurate and versatile, paving the way for more seamless human-computer interactions.

Project Objective

The primary objective of this project is to develop an efficient speech-to-text conversion system tailored for accessibility tools. This system aims to enhance user experience by providing accurate and real-time transcription services, thereby improving the usability of digital devices for individuals with disabilities.

Methodology

1. Data Collection and Preprocessing

• Datasets: Utilize open-source datasets such as LibriSpeech and Common Voice for training and evaluation.
• Preprocessing: Implement noise reduction techniques and normalize audio data to improve transcription accuracy.

2. Model Architecture

• Deep Learning Models: Use state-of-the-art models like Transformer-based architectures to capture the nuances of spoken language.
• Customization: Adapt models to recognize specific vocabularies relevant to accessibility contexts.

3. Training and Evaluation

• Training: Employ supervised learning techniques using labeled datasets.
• Evaluation Metrics: Assess model performance using metrics like Word Error Rate (WER) and accuracy.

Expected Outcomes

The proposed system is expected to deliver high accuracy in converting speech to text, thereby improving accessibility for users with disabilities. By leveraging advanced ASR technologies, the system should facilitate more effective communication and interaction with digital platforms.

Conclusion

This project aims to contribute to the field of accessibility by developing a robust speech-to-text conversion system. By integrating cutting-edge ASR technologies, the project seeks to enhance digital inclusivity for individuals with disabilities, allowing them greater independence and participation in digital environments.

For further details on related research, please refer to the paper "Using Voice Technologies to Support Disabled People," available at ScienceOpen.

For dataset access, you can explore LibriSpeech and Common Voice.