Design and Implementation of a Real-Time Speech-to-Text and Translation System Using Speech Recognition and Natural Language Processing Techniques in Python. (مجلة الفاروق للعلوم, 2026)
This study aims to design and implement a real-time speech-to-text and translation application using speech recognition and natural language processing techniques in Python. The system is developed to provide an integrated solution that converts spoken language into text and optionally translates the recognized content between Arabic and English through a simple and interactive graphical user interface. The application combines speech recognition, machine translation, audio processing, and multithreading techniques to ensure efficient and responsive performance.The system was implemented using Python and several supporting libraries, including SpeechRecognition for speech-to-text conversion, Deep Translator for machine translation, SoundDevice for audio acquisition, and Tkinter for graphical user interface development. The application allows users to select the input language, record speech, display recognized text, perform real-time translation, and automatically save recognized content with timestamps for future reference.The results demonstrate that the proposed application can successfully recognize spoken input and generate translated output in real time while maintaining an accessible and user-friendly environment. The integration of speech recognition and translation technologies provides a practical solution for bilingual communication, language learning, and assistive applications.The findings indicate that combining modern speech recognition and neural machine translation technologies within a single lightweight application can improve usability and accessibility while demonstrating the practical benefits of recent advances in natural language processing and artificial intelligence.