An ensemble-based framework for mispronunciation detection of Arabic phonemes

TL;DR

This paper presents a novel ensemble learning framework for detecting mispronunciations of Arabic phonemes, achieving high accuracy and aiding Arabic language learning.

Contribution

It is the first comprehensive application of ensemble machine learning techniques for Arabic phoneme mispronunciation detection, integrating various feature extraction methods.

Findings

Achieved 95.9% classification accuracy.

Effective use of Mel spectrogram with voting classifier.

Enhanced dataset through noise and pitch modifications.

Abstract

Determination of mispronunciations and ensuring feedback to users are maintained by computer-assisted language learning (CALL) systems. In this work, we introduce an ensemble model that defines the mispronunciation of Arabic phonemes and assists learning of Arabic, effectively. To the best of our knowledge, this is the very first attempt to determine the mispronunciations of Arabic phonemes employing ensemble learning techniques and conventional machine learning models, comprehensively. In order to observe the effect of feature extraction techniques, mel-frequency cepstrum coefficients (MFCC), and Mel spectrogram are blended with each learning algorithm. To show the success of proposed model, 29 letters in the Arabic phonemes, 8 of which are hafiz, are voiced by a total of 11 different person. The amount of data set has been enhanced employing the methods of adding noise, time shifting, time stretching, pitch shifting. Extensive experiment results demonstrate that the utilization of voting classifier as an ensemble algorithm with Mel spectrogram feature extraction technique exhibits remarkable classification result with 95.9% of accuracy.