CFP last date
15 November 2024
Call for Paper
December Edition
IJAIS solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 15 November 2024

Submit your paper
Know more
The week's pick
Responsive image
Ordered Frequent Itemsets Matrix Based On Fp-Tree Structure And Apriori Algorithm

Abdulkader M. Al-Badani Abdualmajed A. Al-Khulaidi
Random Articles
Reseach Article

A Grapheme-based Text to Speech System for Yoruba Language

by Itunuoluwa Isewon, Adejumoke Famade, Jelili Oyelade
journal cover thumbnail

International Journal of Applied Information Systems
Foundation of Computer Science (FCS), NY, USA
Volume 12 - Number 39
Year of Publication: 2022
Authors: Itunuoluwa Isewon, Adejumoke Famade, Jelili Oyelade
10.5120/ijais2022451924

Itunuoluwa Isewon, Adejumoke Famade, Jelili Oyelade . A Grapheme-based Text to Speech System for Yoruba Language. International Journal of Applied Information Systems. 12, 39 ( April 2022), 1-5. DOI=10.5120/ijais2022451924

@article{ 10.5120/ijais2022451924,
author = { Itunuoluwa Isewon, Adejumoke Famade, Jelili Oyelade },
title = { A Grapheme-based Text to Speech System for Yoruba Language },
journal = { International Journal of Applied Information Systems },
issue_date = { April 2022 },
volume = { 12 },
number = { 39 },
month = { April },
year = { 2022 },
issn = { 2249-0868 },
pages = { 1-5 },
numpages = {9},
url = { https://www.ijais.org/archives/volume12/number39/1124-2022451924/ },
doi = { 10.5120/ijais2022451924 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2023-07-05T19:11:21.588042+05:30
%A Itunuoluwa Isewon
%A Adejumoke Famade
%A Jelili Oyelade
%T A Grapheme-based Text to Speech System for Yoruba Language
%J International Journal of Applied Information Systems
%@ 2249-0868
%V 12
%N 39
%P 1-5
%D 2022
%I Foundation of Computer Science (FCS), NY, USA
Abstract

A Text to Speech synthesizer is an application that converts text to speech; the process of conversion is achieved by the application of the analysis of natural and digital signal processing on the input text. Over the years, some Text to Speech systemshas been built for different languages e.g. English, Kiswahili, German, French, Telugu, Mandarin, etc. The aim of this study is to develop an application for Yoruba Text to speech synthesis. The Mary Text to Speech framework was used to implement a speech synthesizer for the Yoruba language. The Text to Speech system developed can convert an input text into speech sound in Yoruba. This application allows a user to input a text and the engine reads out the text as a synthesized speech. The user is also able to upload a text file and the engine also reads the uploaded text file, the user can save the synthesized speech inthe local storage ofthe system. A basic graphical user interface has been produced that plays out the essential elements of a speech synthesizer like conversion and speech synthesis. Agrapheme-based speech synthesizer has been produced for the Yoruba language which is a tonal language.

References
  1. Isewon, I., Oyelade, J., &Oladipupo, O. (2014). Design and Implementation of Text to Speech Conversion for Visually Impaired People. International Journal of Applied Information Systems, 7(2), 25–30.
  2. Dutoit, T., 1993. High quality text-to-speech synthesis ofthe French language. Doctoral dissertation, FacultePolytechnique de Mons.
  3. Duanmu, S. (2004). Tone and Non-Tone Languages: An Alternative to Language Typology and Parameters. Language and Linguistics, 5(4), 891–924.
  4. Ekekwe, N. (2015). Development of Text to Speech Applications. Covenant University.
  5. Byrne, B. (2014). Engineering Part IIB?: Module 4F11 Speech and Language Processing Lectures 9 & 10?: Weighted Finite State Transducers for Speech and Language Processing.
  6. Oloruntoyin, S. T. (2014). DEVELOPMENT OF YORUBA LANGUAGE TEXT-TO-SPEECH E-LEARNING SYSTEM. International Journal of Scholarly Research Gate, 2(1), 19–36.
  7. Shih, C., &Sproat, R. (1996). Issues in text-to-speech conversion for Mandarin. Computational Linguistics and Chinese Language Processing, 1(1), 37–86.
  8. Polomé, E. C. (1967). Swahili language handbook.
  9. Laver, J. D. M. (1970). The production of speech. New Horizons in Linguistics, 53–75. https://doi.org/10.1007/978-1-4613-8202-7_6
  10. Sarroff, A. M., & Casey, M. (2014). Musical Audio Synthesis Using Autoencoding Neural Nets. Proceedings of the International Computer Music Conference, 1(September), 14–20.
  11. Raitio, T. (2008). Hidden Markov Model Based Finnish Text-to-Speech System Utilizing Glottal Inverse Filtering. Master’s Thesis, HELSINKI UNIVERSITY OF TECHNOLOGY. Retrieved from http://lib.tkk.fi/Dipl/2008/urn012274.pdf
  12. Afolabi, A., Omidiora, E., &Arulogun, T. (2013). Development of text to speech system for yoruba language. In Innovative Systems Design and Engineering—Special Issue of 2nd International Conference on Engineering and Technology Research (Vol. 4, pp. 1–8).
  13. Gakuru, M., Iraki, F. K., Tucker, R., Shalonova, K., & Ngugi, K. (2005). Development of a Kiswahili text to speech system. Interspeech-2005, 1481–1484.
  14. Charfuelan, M., Pammi, S., Steiner, I., Pierre, U., Upmc, C., & Tts, M. (2013). MARY TTS unit selection and HMM-based voices for the Blizzard Challenge 2013.
  15. Kamble, K., &Kagalkar, R. (2014). A Review?: Translation of Text to Speech Conversion for Hindi Language, 3(11), 1027–1031.
  16. Schröder, M., &Trouvain, J. (2003a). The German text-to-speech synthesis system MARY: A tool for research, development and teaching. International Journal of Speech Technology, 6(4), 365–377.
  17. Schröder, M., &Trouvain, J. (2003b). The German Text-to-Speech Synthesis System MARY: A Tool for Research, Development and Teaching. International Journal of Speech Technology, 6(4), 365–377. https://doi.org/10.1023/A:1025708916924
  18. Schröder, M., &Trouvain, J. (2003). The German Text-to-Speech Synthesis System MARY: A Tool for Research, Development and Teaching. International Journal of Speech Technology, 6, 365–377. https://doi.org/10.1023/A:1025708916924.
  19. Raitio, T., Suni, A., Yamagishi, J., Pulakka, H., Nurminen, J., Vainio, M., &Alku, P. (2011). HMM-Based Speech Synthesis Utilizing Glottal Inverse Filtering. IEEE Transactions on Audio, Speech, and Language Processing, 19(1), 153–165. https://doi.org/10.1109/TASL.2010.2045239
Index Terms

Computer Science
Information Sciences

Keywords

Text to Speech Speech synthesis Mary tool Yoruba language

Powered by PhDFocusTM