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Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network
International Journal of Fuzzy Logic and Intelligent Systems 2020;20(4):272-277
Published online December 25, 2020
© 2020 Korean Institute of Intelligent Systems.

Jihad Anwar Qadir1, Abdulbasit K. Al-Talabani2, and Hiwa A. Aziz1

1Department of Computer Science, University of Raparin, Rania, Iraq
2Department of Software Engineering, Faculty of Engineering, Koya University, Koya KOY45, Iraq
Correspondence to: Jihad Anwar Qadir (
Received August 28, 2020; Revised November 11, 2020; Accepted November 30, 2020.
This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License ( which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Isolated uttered word recognition has many applications in human–computer interfaces. Feature extraction in speech represents a vital and challenging step for speech-based classification. In this work, we propose a one-dimensional convolutional neural network (CNN) that extracts learned features and classifies them based on a multilayer perceptron. The proposed models are tested on a designed dataset of 119 speakers uttering Kurdish digits (0–9). The results show that both speaker-dependent (average accuracy of 98.5%) and speaker-independent (average accuracy of 97.3%) models achieve convincing results. The analysis of the results shows that 9 of the speakers have a bias characteristic, and their results are outliers compared to the other 110 speakers.
Keywords : Feature extraction, Classification, One-dimensional CNN