Real-Time Speech Recognition for Assistive Technologies: A Case Study

Abstract

This paper presents a case study on the application of real-time speech recognition technologies for assistive devices, focusing on enhancing accessibility and communication for individuals with disabilities. Speech recognition has advanced significantly with the advent of deep learning and natural language processing (NLP), making it increasingly viable for assistive applications. The study evaluates the performance of a speech recognition system integrated into assistive technologies, examining key factors such as accuracy, latency, and user adaptability. We explore various scenarios where real-time voice commands enhance the autonomy of users, such as controlling home appliances, interacting with digital devices, and facilitating communication for individuals with speech or motor impairments. The findings demonstrate the effectiveness of real-time speech recognition in improving the quality of life for people with disabilities, while highlighting challenges such as background noise, speech variability, and device integration. Recommendations for future improvements in robustness and user-centered design are also discussed. This case study contributes to ongoing research in making assistive technologies more accessible and efficient through voice-driven interfaces.