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Youssef Elfahm
University Hassan First
Computer Science & IT Electrical & Electronics Engineering

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Efficiency of the energy contained in modulators in the Arabic vowels recognition Nesrine Abajaddi; Youssef Elfahm; Badia Mounir; Laila Elmaazouzi; Ilham Mounir; Abdelmajid Farchi
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 4: August 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i4.pp3601-3608

Abstract

The speech signal is described as many acoustic properties that may contribute differently to spoken word recognition. Vowel characterization is an important process of studying the acoustic characteristics or behaviors of speech within different contexts. This current study focuses on the modulators characteristics of three Arabic vowels, we proposed a new approach to characterize the three Arabic vowels /a/, /i/ and /u/. The proposed method is based on the energy contained in the speech modulators. The coherent subband demodulation method related to the spectral center of gravity (COG) was used to calculate the energy of the speech modulators. The obtained results showed that the modulators energy help characterize the Arabic vowels /a/, /i/ and /u/ with an interesting recognition rate ranging from 86% to 100%.
Characterization of Arabic sibilant consonants Youssef Elfahm; Nesrine Abajaddi; Badia Mounir; Laila Elmaazouzi; Ilham Mounir; Abdelmajid Farchi
International Journal of Electrical and Computer Engineering (IJECE) Vol 13, No 2: April 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v13i2.pp1997-2008

Abstract

The aim of this study is to develop an automatic speech recognition system in order to classify sibilant Arabic consonants into two groups: alveolar consonants and post-alveolar consonants. The proposed method is based on the use of the energy distribution, in a consonant-vowel type syllable, as an acoustic cue. The application of this method on our own corpus reveals that the amount of energy included in a vocal signal is a very important parameter in the characterization of Arabic sibilant consonants. For consonants classifications, the accuracy achieved to identify consonants as alveolar or post-alveolar is 100%. For post-alveolar consonants, the rate is 96% and for alveolar consonants, the rate is over 94%. Our classification technique outperformed existing algorithms based on support vector machines and neural networks in terms of classification rate.
Co-Authors Abdelmajid Farchi Badia Mounir Ilham Mounir Laila Elmaazouzi Nesrine Abajaddi