Design and Implementation of a Real-Time Speech-to-Text and Translation System Using Speech Recognition and Natural Language Processing Techniques in Python.
DOI:
https://doi.org/10.65405/va2ba550الكلمات المفتاحية:
Speech Recognition, Natural Language Processing, Machine Translation, Speech-to-Text, Real-Time Translation, Python, Neural Machine Translation.الملخص
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.
التنزيلات
المراجع
Bahdanau, D., Cho, K., & Bengio, Y. (2015). Neural Machine Translation by Jointly Learning to Align and Translate. Proceedings of the International Conference on Learning Representations (ICLR 2015). arXiv:1409.0473.
Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., & Amodei, D. (2020). Language Models are Few-Shot Learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
Cho, K., Van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734.
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of NAACL-HLT 2019, 4171–4186.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. Cambridge, MA: MIT Press.
Latif, M. B., & Saeid, S. A. M. (2026). The Impact of Teaching English Literature as an Elective Course to Fourth-Year ESL Students in a University Context. Al-Farooq Journal of Sciences, 2(3), 1166-1179.
Hinton, G., Deng, L., Yu, D., Dahl, G. E., Mohamed, A., Jaitly, N., Senior, A., Vanhoucke, V., Nguyen, P., Sainath, T. N., & Kingsbury, B. (2012). Deep Neural Networks for Acoustic Modeling in Speech Recognition. IEEE Signal Processing Magazine, 29(6), 82–97.
Jurafsky, D., & Martin, J. H. (2023). Speech and Language Processing (3rd ed. draft). Stanford University.
Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving Language Understanding by Generative Pre-Training. OpenAI Technical Report.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 5998–6008.











