Correct pronunciation detection for classical Arabic phonemes using deep learning

Alqadheeb, Fatimah and Asif, Amna and Ahmad, Hafiz Farooq (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. 2021 International Conference of Women in Data Science at Taif University, WiDSTaif 2021 . IEEE, SAU. ISBN 9781665449496

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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.

Item Type:
Contribution in Book/Report/Proceedings
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1706
Subjects:
ID Code:
183377
Deposited By:
Deposited On:
13 Jan 2023 14:00
Refereed?:
Yes
Published?:
Published
Last Modified:
13 Jan 2023 14:00