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All Journal International Journal of Electrical and Computer Engineering
Anas Quteishat
Al Balqa Applied University Sohar University
Computer Science & IT Electrical & Electronics Engineering

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Intelligent Arabic letters speech recognition system based on mel frequency cepstral coefficients Anas Quteishat; Mahmoud Younis; Ahmed Qtaishat; Anmar Abuhamdah
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 3: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i3.pp3348-3358

Abstract

Speech recognition is one of the important applications of artificial intelligence (AI). Speech recognition aims to recognize spoken words regardless of who is speaking to them. The process of voice recognition involves extracting meaningful features from spoken words and then classifying these features into their classes. This paper presents a neural network classification system for Arabic letters. The paper will study the effect of changing the multi-layer perceptron (MLP) artificial neural network (ANN) properties to obtain an optimized performance. The proposed system consists of two main stages; first, the recorded spoken letters are transformed from the time domain into the frequency domain using fast Fourier transform (FFT), and features are extracted using mel frequency cepstral coefficients (MFCC). Second, the extracted features are then classified using the MLP ANN with back-propagation (BP) learning algorithm. The obtained results show that the proposed system along with the extracted features can classify Arabic spoken letters using two neural network hidden layers with an accuracy of around 86%.
Co-Authors Ahmed Qtaishat Anmar Abuhamdah Mahmoud Younis