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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 (jihad.qadir@uor.edu.krd)
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 (http://creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract
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