SPEECH-TO-TEXT: A SECURED REAL-TIME LANGUAGE TRANSLATION PLATFORM FOR STUDENTS

  • Eluemunor Kizito Anazia Delta State University of Science and Technology, Ozoro
  • Erife Friday Eti
  • Peter Henry Ovili
  • O. Francis Ogbimi
Keywords: Boostrap, Google Cloud Translation, JavaScript, Hypertext Markup Language, Speech- to-Text Translation

Abstract

In order to establish effective communication and understanding among students regardless of language background, there is need to develop a common platform that will support this motive. This necessity has led to the emergence of Speech-to-Text (S-to-T) translation framework that enabling students with diverse languages to communicate directly without relying on intermediaries. English has become the foremost lingua franca in Nigeria, spoken widely across various ethnic groups. However, this continuous use of English language has affected the subsistence of indigenous Nigerian languages, leading to many children growing up unable to speak their native language. In modern, civilized societies, speech remains a primary and essential means of communication, allowing individuals to express a range of ideas from their minds through organized words, phrases, and sentences that follow grammatical rules. This work, Speech-to-Text: A secure real-time language translation platform for students, translates speech to English and Yoruba languages during chat. It was developed using ASP.Net with C# as the base technology. The model was developed with CSS, Bootstrap, JQuery, and JavaScript, ensuring responsiveness, while a secure SQL Server database repository supports data storage. The software is structured using the Object-Oriented Methodology (OOM). This platform presents a user-friendly and intuitive web interface that allows students of both English and Yoruba speakers to easily access and interact with other in real-time thereby bridging the communication gap between them.

References

Adebara, I., Abdul-Mageed, M. and Silfverberg, M. (2022). Linguistically-motivated Yorb-English machine translation. In Proceedings of the 29th International Conference on Computational Linguistics (pp. 5066-5075). (Bird et al., 2020).

Adekunle, O., Agbonifo, O. and Olaniyan, J. (2020). Development of Bi-Directional English to Yoruba Translator for Real-Time Mobile Chatting. International Journal of Computational Linguistics, 11(1).

Adewole, L. B., Adetunmbi, A. O., Alese, B. K. and Oluwadare, S. A. (2017). Token Validation in Automatic Corpus Gathering for Yoruba Language. FUOYE Journal of Engineering and Technology, 2(1), 4. DOI: https://doi.org/10.46792/fuoyejet.v2i1.85

Adigun, O., Rufai, M. M., Okikiola, F. M. and Olukumoro, S. (2024). Machine Learning Techniques for Prediction Of Covid-19 In Potential Patients, Vol. 7 No. 4 / DOI: https://doi.org/10.33003/fjs-2024-0804-2579 DOI: https://doi.org/10.33003/fjs-2023-0704-1901

Akintola, A. and Ibiyemi, T. (2017). Machine to Man Communication in Yorb Language. Annal. Comput. Sci. Ser, 15(2).

Akintola, A. and Ibiyemi, T. (2017). Machine to Man Communication in Yorb Language. Annal. Comput. Sci. Ser, 15(2).

Akinwale, O. I., Adetunmbi, A. O., Obe, O. O. and Adesuyi, A. (2015). Web-based English to Yoruba Machine Translation. International Journal of Language and Linguistics, 3(3), 154-159. DOI: https://doi.org/10.11648/j.ijll.20150303.17

Ajao, J., Yusuff, S. and Ajao, A. (2022). Yorb Character Recognition System Using Convolutional Recurrent Neural Network. Black Sea Journal of Engineering and Science, 5(4), 151-157. DOI: https://doi.org/10.34248/bsengineering.1125590

Ajibade, B. and Eludiora, S. (2021). Design and Implementation of English to Yorub'a Verb Phrase Machine Translation System. arXiv preprint arXiv: 2104.04125.

Amin, E. A. R. (2022). Using repeated-reading and listeningwhile-reading via text-to-speech apps in developing fluency and comprehension. World Journal of English Language, 12(1). DOI: https://doi.org/10.5430/wjel.v12n1p211

Benjamin, A. and Eludiora, S. (2020). Design and Implementation of English to Yorb Verb Phrase Machine Translation System. ACL 2020 Submission.

Chauhan, V., Dwivedi, S., Karale, P. and Potdar, S. M. (2016). Speech to Text Converter Using Gaussian Mixture Model (GMM). International Research Journal of Engineering and Technology (IRJET), 3(2), e-ISSN: 2395-0056.

Eludiora, S. I. and Odejobi, O. A. (2016). Development of English to Yorb Machine Translator. International Journal of Modern Education and Computer Science, 8(11), 8. DOI: https://doi.org/10.5815/ijmecs.2016.11.02

Esan, A., Oladosu, J., Oyeleye, C., Adeyanju, I., Olaniyan, O., Okomba, N. and Adanigbo, O. (2020). Development of a recurrent neural network model for English to Yorb machine translation. Development, 11(5). DOI: https://doi.org/10.14569/IJACSA.2020.0110574

Graves, A., Liwicki, M., Fernandez, S., Bertolami, R., Bunke, H. and Schmidhuber, J. (2013). Handwriting Recognition With Recurrent Neural Networks. Advances in Neural Information Processing Systems.

Madahana, M. (2022). A Proposed Artificial Intelligence-Based Real-Time Speech-To-Text to Sign Language Translator for South African Official Languages for The Covid-19 Era and Beyond: In Pursuit Of Solutions For The Hearing Impaired. South African Journal of Communication Disorders, 69(2). DOI: https://doi.org/10.4102/sajcd.v69i2.915

Oloruntoyin, S. T. (2014). Development of Yoruba Language Text-to-Speech e-Learning System. International Journal of Scholarly Research Gate, 2(1), 1936.

Padmane, P. (2022). Multilingual Speech and Text Recognition and Translation. International Journal of Innovations in Engineering and Science, 7(8). DOI: https://doi.org/10.46335/IJIES.2022.7.8.15

Prachi, K. and Bhope, V. (2015). Implementation of Speech to Text Conversion. International Journal of Innovative Research in Science, Engineering and Technology, 4(7).

Ren, Y., Ruan, Y., Tan, X., Qin, T., Zhao, S., Zhao, Z. and Liu, T. Y. (2019). Fastspeech: Fast, robust and controllable text to speech. Advances in Neural Information Processing Systems, 32.

Sanchit, C., Aniket, S. and Tanvi, G. (2022). Real-Time Direct Speech-To-Speech Translation. International Research Journal of Engineering and Technology, 9, Jan 2022.

Sawai, R., Paik, I. and Kuwana, A. (2021). Sentence augmentation for language translation using gpt-2. Electronics, 10(24), 3082. DOI: https://doi.org/10.3390/electronics10243082

Sneha, B., Himanshi, A., Shreya, J., Shilpa, G., Mrinal, B., Biswajeet, P. and Mazen, A. (2023). Challenges and Limitations in Speech Recognition Technology: A Critical Review of Speech Signal Processing Algorithms, Tools and Systems, Computer Modeling in Engineering and Sciences 2023, 135(2), 1053-1089. https://doi.org/10.32604/cmes.2022.02175 DOI: https://doi.org/10.32604/cmes.2022.021755

Siddique, L., Aun, Z., Heriberto, C., Fahad, S., Moazzam, S. and Junaid, Q. (2023). Transformers in Speech Processing: A Survey. Computation and Language (cs.CL); Sound (cs.SD); Audio and Speech Processing (eess.AS).

Totare, Y., Sarthak, B., Sandesh, C., Sumit, D., Prathamesh, G. and Anurag, T. (2023). Speech Translation Using Machine Learning. International Research Journal of Modernization in Engineering Technology and Science, 5, May 2023.

Vasilakes, J., Zhou, S. and Zhang, R. (2020). Natural Language Processing. Elsevier Institute for Health Informatics Surgery, ISBN: 9780128202739. DOI: https://doi.org/10.1016/B978-0-12-820273-9.00006-3

Xiao, T. and Zhu, J. (2023). Introduction to Transformers: an NLP Perspective. arXiv preprint arXiv:2311.17633.

Published
2024-12-14
How to Cite
AnaziaE. K., EtiE. F., OviliP. H., & Ogbimi O. F. (2024). SPEECH-TO-TEXT: A SECURED REAL-TIME LANGUAGE TRANSLATION PLATFORM FOR STUDENTS. FUDMA JOURNAL OF SCIENCES, 8(6), 329 - 338. https://doi.org/10.33003/fjs-2024-0806-2890