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A Yoruba Language Automatic Speech Recognition System using Deep Learning Approach

by Ogunjobi Olumide Jospeh, Agbonifo Oluwatoyin Catherine, Akinwonmi Akintoba E.
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 124
Year of Publication: 2026
Authors: Ogunjobi Olumide Jospeh, Agbonifo Oluwatoyin Catherine, Akinwonmi Akintoba E.
10.5120/ijca0dcb3a1b147c

Ogunjobi Olumide Jospeh, Agbonifo Oluwatoyin Catherine, Akinwonmi Akintoba E. . A Yoruba Language Automatic Speech Recognition System using Deep Learning Approach. International Journal of Computer Applications. 187, 124 ( Jul 2026), 60-65. DOI=10.5120/ijca0dcb3a1b147c

@article{ 10.5120/ijca0dcb3a1b147c,
author = { Ogunjobi Olumide Jospeh, Agbonifo Oluwatoyin Catherine, Akinwonmi Akintoba E. },
title = { A Yoruba Language Automatic Speech Recognition System using Deep Learning Approach },
journal = { International Journal of Computer Applications },
issue_date = { Jul 2026 },
volume = { 187 },
number = { 124 },
month = { Jul },
year = { 2026 },
issn = { 0975-8887 },
pages = { 60-65 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number124/a-yoruba-language-automatic-speech-recognition-system-using-deep-learning-approach/ },
doi = { 10.5120/ijca0dcb3a1b147c },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-07-29T00:34:04+05:30
%A Ogunjobi Olumide Jospeh
%A Agbonifo Oluwatoyin Catherine
%A Akinwonmi Akintoba E.
%T A Yoruba Language Automatic Speech Recognition System using Deep Learning Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 124
%P 60-65
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper, a Yorùbá Language Automatic Speech Recognition System capable of recognizing words spoken by the users based on preprocessed stored data was designed and implemented. Dataset from Data Science and Computational Research Laboratory, Federal University of Technology, Akure, Nigeria, were made used of. Speech feature sequences were extracted using Mel-frequency cepstral coefficients (MFCC) technique. Kaggle environment was deployed, where python programming language was used to implement the Transformer and LSTM (Long Short-Term Memory) models. The preprocessed data were fed into both models for training, both models were tested and evaluated across the following standard evaluation metrics WER, MER and CER. The results obtained from both models shows a promising approach that could be adopted for Yorùbá Language speech recognition system. The research work describes ASR for standard Yorúbà language. The source language and target language is Yorùbá language, with focus on speaking in Yorùbá language (voice data) and the output in Yorùbá language text form (text data).

References
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Index Terms

Computer Science
Information Sciences

Keywords

Automatic Speech Recognition (ASR) Mel-Frequency Cepstral Coefficients (MFCC) Speech-to-Text Low-Resource Language Deep Learning

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