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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2411.13592 |
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| _version_ | 1866910707031736320 |
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| author | Berriche, Lamia Driss, Maha Almuntashri, Areej Ahmed Lghabi, Asma Mufreh Almudhi, Heba Saleh Almansour, Munerah Abdul-Aziz |
| author_facet | Berriche, Lamia Driss, Maha Almuntashri, Areej Ahmed Lghabi, Asma Mufreh Almudhi, Heba Saleh Almansour, Munerah Abdul-Aziz |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_13592 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children Berriche, Lamia Driss, Maha Almuntashri, Areej Ahmed Lghabi, Asma Mufreh Almudhi, Heba Saleh Almansour, Munerah Abdul-Aziz Sound Artificial Intelligence 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. |
| title | A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children |
| topic | Sound Artificial Intelligence |
| url | https://arxiv.org/abs/2411.13592 |