Isolated Spoken Word Recognition Using One-Dimensional Convolutional Neural Network

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.