Correct pronunciation detection for classical Arabic phonemes using deep learning

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Published

Standard

Correct pronunciation detection for classical Arabic phonemes using deep learning. / Alqadheeb, Fatimah; Asif, Amna; Ahmad, Hafiz Farooq.
2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021. IEEE, 2021. 9430236 (2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021).

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSN › Conference contribution/Paper › peer-review

Harvard

Alqadheeb, F, Asif, A & Ahmad, HF 2021, Correct pronunciation detection for classical Arabic phonemes using deep learning. in 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021., 9430236, 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021, IEEE, 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021, Taif, Saudi Arabia, 30/03/21. https://doi.org/10.1109/WIDSTAIF52235.2021.9430236

APA

Alqadheeb, F., Asif, A., & Ahmad, H. F. (2021). Correct pronunciation detection for classical Arabic phonemes using deep learning. In 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021 Article 9430236 (2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021). IEEE. https://doi.org/10.1109/WIDSTAIF52235.2021.9430236

Vancouver

Alqadheeb F, Asif A, Ahmad HF. Correct pronunciation detection for classical Arabic phonemes using deep learning. In 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021. IEEE. 2021. 9430236. (2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021). Epub 2021 Mar 30. doi: 10.1109/WIDSTAIF52235.2021.9430236

Author

Alqadheeb, Fatimah ; Asif, Amna ; Ahmad, Hafiz Farooq. / Correct pronunciation detection for classical Arabic phonemes using deep learning. 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021. IEEE, 2021. (2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021).

Bibtex

@inproceedings{046d539882a145eeb2388b798c164c8e,

title = "Correct pronunciation detection for classical Arabic phonemes using deep learning",

abstract = "The pronunciation of the Arabic language is required all articulatory phonetics organs to formulate the correct sounds of a word. It is challenging for non-native Arabic speakers to learn to recite the Holy Quran with correct “Tajweed” rules and pronunciation. The limited contributions are made in the development of classic Arabic short vowels dataset that hinder the development of speech recognition system to facilitate the learner during their Holy Quran learning process. Therefore, it is required to have a collection of audio classic Arabic datasets that can help speech recognition and mispronouncing detection of the classic Arabic speech. In this paper, we aim to collect the classical Arabic alphabet with short vowels. Short vowels are an essential part of the Arabic language. One word of the Arabic language consists of at least one or two short vowels. First, we start with requirement gathering for the classical Arabic short vowels. Our primary focus is to record and process the collected audio dataset. The first release of the audio dataset collected consists of 2892 Arabic alphabet short vowels. A significant effort is applied in preprocessing of the dataset consisting of 84 classes of the Arabic alphabet short vowels. Then, the dataset is tested using a sequential convolution neural network (CNN) on 312 phonemes of the collected Arabic Alphabet /a/ {"}Alif{"} with short vowels. The result shows that CNN gives high testing accuracy of 100% and a loss of 0.27.",

keywords = "Audio processing, Classic Arabic audio dataset, Classical Arabic, Deep learning",

author = "Fatimah Alqadheeb and Amna Asif and Ahmad, {Hafiz Farooq}",

year = "2021",

month = may,

day = "24",

doi = "10.1109/WIDSTAIF52235.2021.9430236",

language = "English",

isbn = "9781665449496",

series = "2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021",

publisher = "IEEE",

booktitle = "2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021",

note = "2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021 ; Conference date: 30-03-2021 Through 31-03-2021",

}

RIS

TY - GEN

T1 - Correct pronunciation detection for classical Arabic phonemes using deep learning

AU - Alqadheeb, Fatimah

AU - Asif, Amna

AU - Ahmad, Hafiz Farooq

PY - 2021/5/24

Y1 - 2021/5/24

N2 - The pronunciation of the Arabic language is required all articulatory phonetics organs to formulate the correct sounds of a word. It is challenging for non-native Arabic speakers to learn to recite the Holy Quran with correct “Tajweed” rules and pronunciation. The limited contributions are made in the development of classic Arabic short vowels dataset that hinder the development of speech recognition system to facilitate the learner during their Holy Quran learning process. Therefore, it is required to have a collection of audio classic Arabic datasets that can help speech recognition and mispronouncing detection of the classic Arabic speech. In this paper, we aim to collect the classical Arabic alphabet with short vowels. Short vowels are an essential part of the Arabic language. One word of the Arabic language consists of at least one or two short vowels. First, we start with requirement gathering for the classical Arabic short vowels. Our primary focus is to record and process the collected audio dataset. The first release of the audio dataset collected consists of 2892 Arabic alphabet short vowels. A significant effort is applied in preprocessing of the dataset consisting of 84 classes of the Arabic alphabet short vowels. Then, the dataset is tested using a sequential convolution neural network (CNN) on 312 phonemes of the collected Arabic Alphabet /a/ "Alif" with short vowels. The result shows that CNN gives high testing accuracy of 100% and a loss of 0.27.

AB - The pronunciation of the Arabic language is required all articulatory phonetics organs to formulate the correct sounds of a word. It is challenging for non-native Arabic speakers to learn to recite the Holy Quran with correct “Tajweed” rules and pronunciation. The limited contributions are made in the development of classic Arabic short vowels dataset that hinder the development of speech recognition system to facilitate the learner during their Holy Quran learning process. Therefore, it is required to have a collection of audio classic Arabic datasets that can help speech recognition and mispronouncing detection of the classic Arabic speech. In this paper, we aim to collect the classical Arabic alphabet with short vowels. Short vowels are an essential part of the Arabic language. One word of the Arabic language consists of at least one or two short vowels. First, we start with requirement gathering for the classical Arabic short vowels. Our primary focus is to record and process the collected audio dataset. The first release of the audio dataset collected consists of 2892 Arabic alphabet short vowels. A significant effort is applied in preprocessing of the dataset consisting of 84 classes of the Arabic alphabet short vowels. Then, the dataset is tested using a sequential convolution neural network (CNN) on 312 phonemes of the collected Arabic Alphabet /a/ "Alif" with short vowels. The result shows that CNN gives high testing accuracy of 100% and a loss of 0.27.

KW - Audio processing

KW - Classic Arabic audio dataset

KW - Classical Arabic

KW - Deep learning

U2 - 10.1109/WIDSTAIF52235.2021.9430236

DO - 10.1109/WIDSTAIF52235.2021.9430236

M3 - Conference contribution/Paper

AN - SCOPUS:85107494930

SN - 9781665449496

T3 - 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021

BT - 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021

PB - IEEE

T2 - 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021

Y2 - 30 March 2021 through 31 March 2021

ER -

Research

Links

Text available via DOI:

Keywords