Comparative Analysis of Arabic Vowels using Formants and an Automatic Speech Recognition System

Abstract

Arabic, the world's second most spoken language in terms of number of speakers, has not received much attention from the traditional speech processing research community. This study is specifically concerned with the analysis of vowels in modern standard Arabic dialect. The first and second formant values in these vowels are investigated and the differences and similarities between the vowels explored using consonant-vowels-consonant (CVC) utterances. For this purpose, a Hidden Markov Model (HMM) based recognizer is built to classify the vowels and the performance of the recognizer analyzed to help understand the similarities and dissimilarities between the phonetic features of vowels. The vowels are also analyzed in both time and frequency domains, and the consistent findings of the analysis are expected to enable future Arabic speech processing tasks such as vowel and speech recognition and classification.