Exploring Arabic non-sibilant fricative consonants: a comprehensive classification

Abstract

Voice interaction serves as an efficient tool for improving communication between humans and machines, enabling quicker exchanges without the need for physical contact with devices. This study introduces a methodology aimed at identifying Arabic non-sibilant fricative consonants and recognizing their significance within this context. Our approach involves analyzing the normalized energy distribution within consonant-vowel phonetic syllables, which serves as the acoustic input signal for our speech recognition system. Notably, the relatively limited attention given to non-sibilant consonants in existing literature underscores the importance of this research endeavor. The evaluation of our classification method applied to our dataset highlights the pivotal role of vocal signal energy in characterizing Arabic non-sibilant fricative consonants. Achieving a classification accuracy of 90% for distinguishing voiced and voiceless non-sibilant consonants, with a rate exceeding 85% for other categories, underscores the effectiveness of our approach. Noteworthy is the outperformance of our classification technique compared to current algorithms based on support vector machines (SVM) and artificial neural networks (ANNs), indicating its potential to enhance Arabic automatic speech recognition (ASR) systems. Overall, this research contributes to advancing speech recognition technology, particularly in the realm of Arabic linguistics, offering promising implications for future developments in this field.