Speech-to-Text Hybrid English to Yoruba SMS Translator

  • Akinbowale Nathaniel Babatunde Kwara State University, Malete, Kwara State, Nigeria
  • Ronke Seyi Babatunde Kwara State University, Malete, Kwara State, Nigeria
  • Bukola Fatimah Balogun Kwara State University, Malete, Kwara State, Nigeria
  • Emmanuel Umar Tai Solarin University of Education, Ogun state
  • Shuaib Babatunde Mohammed Kwara State University, Malete, Kwara State, Nigeria
  • Afeez Adeshina Oke Federal College of Education, Iwo, Osun State
  • Kolawole Yusuf Obiwusi Summit University, Offa, Kwara State, Nigeria
Keywords: Google translator, machine translation, natural language processing, speech synthesizer, translation, Yoruba language

Abstract

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The necessity for quick translation from English to regional languages, such as Yoruba language arises from the fact that it is imperative for the widespread dissemination of knowledge and information. A speech-to-text enabled translation on Android devices and text-to-speech synthesizer (TTS) capable to deliver real-time translation of Yoruba language is presented. It increases the naturalness of oral communications, eases the communication, and furthers the understanding and use of Yoruba language among people of different backgrounds. This speech-to-text enabled translation system uses NLP on one hand and a TTS interpretation technique on the other. It uses an Android Google translation API synthesizer and a recognizer for English and Yoruba translation. The current study attempted to develop an English to Yoruba SMS translator with built-in text-to-speech and speech-to-text features. The Android Studio Integrated Development Environment was used to create the system (IDE). Whereas, a statistical machine translation algorithm was adopted for the implementation of Google API translation for English speech to text, TTS conversions, and Yoruba translations. About 100 computational rules were formulated for the Yoruba voice translation process. For the translation processes, a questionnaire was also created and circulated to 350 people. Approximately, 96% of those who responded provided input. The responses were subjected to SPSS statistical analysis. The results suggested that the translation system has a 92% accuracy which is comparable to that of a human (expert).

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Published
2024-06-19
How to Cite
Babatunde, A. N., Babatunde, R. S., Balogun, B. F., Umar, E., Mohammed, S. B., Oke, A. A., & Obiwusi, K. Y. (2024). Speech-to-Text Hybrid English to Yoruba SMS Translator . Innovative Computing Review, 4(1), 15-36. https://doi.org/10.32350/icr.41.02