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Badia Mounir
University Cadi Ayyad
Computer Science & IT Electrical & Electronics Engineering

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Emotion recognition from syllabic units using k-nearest-neighbor classification and energy distribution Abdellah Agrima; Ilham Mounir; Abdelmajid Farchi; Laila Elmaazouzi; Badia Mounir
International Journal of Electrical and Computer Engineering (IJECE) Vol 11, No 6: December 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v11i6.pp5438-5449

Abstract

In this article, we present an automatic technique for recognizing emotional states from speech signals. The main focus of this paper is to present an efficient and reduced set of acoustic features that allows us to recognize the four basic human emotions (anger, sadness, joy, and neutral). The proposed features vector is composed by twenty-eight measurements corresponding to standard acoustic features such as formants, fundamental frequency (obtained by Praat software) as well as introducing new features based on the calculation of the energies in some specific frequency bands and their distributions (thanks to MATLAB codes). The extracted measurements are obtained from syllabic units’ consonant/vowel (CV) derived from Moroccan Arabic dialect emotional database (MADED) corpus. Thereafter, the data which has been collected is then trained by a k-nearest-neighbor (KNN) classifier to perform the automated recognition phase. The results reach 64.65% in the multi-class classification and 94.95% for classification between positive and negative emotions.
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%.
Energy distribution in formant bands for arabic vowels Mohamed Farchi; Karim Tahiry; Soufyane Mounir; Badia Mounir; Ahmed Mouhsen
International Journal of Electrical and Computer Engineering (IJECE) Vol 9, No 2: April 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (502.674 KB) | DOI: 10.11591/ijece.v9i2.pp1163-1167

Abstract

The acoustic cues play a major role in speech segmentation phase; the extraction of these indexes facilitates the characterization of the speech signal. In this work, we aim to study Arabic vowels (/a/, /a:/, /i/, /i:/, /u/ and /u:/), especially the long ones. We are interested in characterizing this type of vowels in terms of time, frequency and energy. The cues extracted and analyzed in this work are: segment length, voicing degree and formants values.
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.
Emotion recognition based on the energy distribution of plosive syllables Abdellah Agrima; Ilham Mounir; Abdelmajid Farchi; Laila Elmazouzi; Badia Mounir
International Journal of Electrical and Computer Engineering (IJECE) Vol 12, No 6: December 2022
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v12i6.pp6159-6171

Abstract

We usually encounter two problems during speech emotion recognition (SER): expression and perception problems, which vary considerably between speakers, languages, and sentence pronunciation. In fact, finding an optimal system that characterizes the emotions overcoming all these differences is a promising prospect. In this perspective, we considered two emotional databases: Moroccan Arabic dialect emotional database (MADED), and Ryerson audio-visual database on emotional speech and song (RAVDESS) which present notable differences in terms of type (natural/acted), and language (Arabic/English). We proposed a detection process based on 27 acoustic features extracted from consonant-vowel (CV) syllabic units: \ba, \du, \ki, \ta common to both databases. We tested two classification strategies: multiclass (all emotions combined: joy, sadness, neutral, anger) and binary (neutral vs. others, positive emotions (joy) vs. negative emotions (sadness, anger), sadness vs. anger). These strategies were tested three times: i) on MADED, ii) on RAVDESS, iii) on MADED and RAVDESS. The proposed method gave better recognition accuracy in the case of binary classification. The rates reach an average of 78% for the multi-class classification, 100% for neutral vs. other cases, 100% for the negative emotions (i.e. anger vs. sadness), and 96% for the positive vs. negative emotions.
Co-Authors Abdellah Agrima Abdelmajid Farchi Ahmed Mouhsen Ilham Mounir Karim Tahiry Laila Elmaazouzi Laila Elmazouzi Mohamed Farchi Nesrine Abajaddi Soufyane Mounir Youssef Elfahm