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arXiv · 2411.13592

A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children

Lamia Berriche·Maha Driss·Areej Ahmed Almuntashri·Asma Mufreh Lghabi·Heba Saleh Almudhi·Munerah Abdul-Aziz Almansour
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Abstract

This paper introduces a new application named ArPA for Arabic kids who have trouble with pronunciation. Our application comprises two key components: the diagnostic module and the therapeutic module. The diagnostic process involves capturing the child's speech signal, preprocessing, and analyzing it using different machine learning classifiers like K-Nearest Neighbors (KNN), Support Vector Machine (SVM), and Decision Trees as well as deep neural network classifiers like ResNet18. The therapeutic module offers eye-catching gamified interfaces in which each correctly spoken letter earns a higher avatar level, providing positive reinforcement for the child's pronunciation improvement. Two datasets were used for experimental evaluation: one from a childcare centre and the other including Arabic alphabet pronunciation recordings. Our work uses a novel technique for speech recognition using Melspectrogram and MFCC images. The results show that the ResNet18 classifier on speech-to-image converted data effectively identifies mispronunciations in Arabic speech with an accuracy of 99.015\% with Mel-Spectrogram images outperforming ResNet18 with MFCC images.

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Lamia Berriche, Maha Driss, Areej Ahmed Almuntashri, Asma Mufreh Lghabi, Heba Saleh Almudhi, Munerah Abdul-Aziz Almansour. 2024-11-18. A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children. https://arxiv.org/abs/2411.13592

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