Open AccessClassification of Arabic fricative consonants according to their places of articulation
Author(s) -
Youssef Elfahm,
Nesrine Abajaddi,
Badia Mounir,
Laila Elmaazouzi,
Ilham Mounir,
Abdelmajid Farchi
Publication year - 2022
Publication title -
international journal of power electronics and drive systems/international journal of electrical and computer engineering
Language(s) - English
Resource type - Journals
eISSN - 2722-2578
pISSN - 2722-256X
DOI - 10.11591/ijece.v12i1.pp936-945
Subject(s) - speech recognition , computer science , manner of articulation , vowel , syllable , point (geometry) , consonant , place of articulation , arabic , artificial intelligence , energy (signal processing) , natural language processing , mathematics , linguistics , statistics , philosophy , geometry
Many technology systems have used voice recognition applications to transcribe a speaker’s speech into text that can be used by these systems. One of the most complex tasks in speech identification is to know, which acoustic cues will be used to classify sounds. This study presents an approach for characterizing Arabic fricative consonants in two groups (sibilant and non-sibilant). From an acoustic point of view, our approach is based on the analysis of the energy distribution, in frequency bands, in a syllable of the consonant-vowel type. From a practical point of view, our technique has been implemented, in the MATLAB software, and tested on a corpus built in our laboratory. The results obtained show that the percentage energy distribution in a speech signal is a very powerful parameter in the classification of Arabic fricatives. We obtained an accuracy of 92% for non-sibilant consonants /f, χ, ɣ, ʕ, ћ, and h/, 84% for sibilants /s, sҁ, z, Ӡ and ∫/, and 89% for the whole classification rate. In comparison to other algorithms based on neural networks and support vector machines (SVM), our classification system was able to provide a higher classification rate.