A Dataset for Speech Recognition to Support Arabic Phoneme Pronunciation

Arafa, Moner N. M.; Elbarougy, Reda; Ewees, Ahmed A.; Behery, G. M.

doi:10.5815/ijigsp.2018.04.04

Cited by 12 publications

(3 citation statements)

References 8 publications

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“…Te comparative analysis of these studies is summarized in Table 1. Few studies [18][19][20] also used deep learning algorithms for Arabic speech recognition and correct pronunciation. Besides these, some studies provide surveys of Al-Quran and Arabic language recognition [21][22][23].…”

Section: Related Work a Lot Of Work Has Been Done To Classifymentioning

confidence: 99%

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

Nigar,

Wajid,

Ajagbe

et al. 2023

Applied Computational Intelligence and Soft Computing

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The COVID-19 pandemic influenced the whole world and changed social life globally. Social distancing is an effective strategy adopted by all countries to prevent humans from being infected. Al-Quran is the holy book of Muslims and its listening and reading is one of the obligatory activities. Close contact is essential in traditional learning system; however, most of the Al-Quran learning schools were locked down to minimize the spread of COVID-19 infection. To address this limitation, in this paper, we propose a novel system using deep learning to identify the correct recitation of individual alphabets, words from a recited verse and a complete verse of Al-Quran to assist the reciter. Moreover, in the proposed approach, if the user recites correctly, his/her voice is also added to the existing dataset to leverage proposed approach effectiveness. We employ mel-frequency cepstral coefficients (MFCC) to extract voice features and long short-term memory (LSTM), a recurrent neural network (RNN) for classification. The said approach is validated using the Al-Quran dataset. The results demonstrate that the proposed system outperforms the state-of-the-art approaches with an accuracy rate of 97.7%. This system will help the Muslim community all over the world to recite the Al-Quran in the right way in the absence of human help due to similar future pandemics.

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Section: Related Work a Lot Of Work Has Been Done To Classifymentioning

confidence: 99%

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

Nigar,

Wajid,

Ajagbe

et al. 2023

Applied Computational Intelligence and Soft Computing

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“…The technique lowered false-acceptance and false-rejection rates by 26% and 39% compared to the GOP technique. Furthermore, in [6], the authors presented a speech recognition system capable of detecting mispronunciations. The dataset contains 89 students, 46 of whom are female.…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Mispronunciation Recognition System Using LSTM Network

et al. 2023

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The Arabic language has always been an immense source of attraction to various people from different ethnicities by virtue of the significant linguistic legacy that it possesses. Consequently, a multitude of people from all over the world are yearning to learn it. However, people from different mother tongues and cultural backgrounds might experience some hardships regarding articulation due to the absence of some particular letters only available in the Arabic language, which could hinder the learning process. As a result, a speaker-independent and text-dependent efficient system that aims to detect articulation disorders was implemented. In the proposed system, we emphasize the prominence of “speech signal processing” in diagnosing Arabic mispronunciation using the Mel-frequency cepstral coefficients (MFCCs) as the optimum extracted features. In addition, long short-term memory (LSTM) was also utilized for the classification process. Furthermore, the analytical framework was incorporated with a gender recognition model to perform two-level classification. Our results show that the LSTM network significantly enhances mispronunciation detection along with gender recognition. The LSTM models attained an average accuracy of 81.52% in the proposed system, reflecting a high performance compared to previous mispronunciation detection systems.

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“…Recently, number of Arabic corpora types is presented such as Raw Text Corpora, Annotated Corpora, Lexical Databases Speech Corpora and handwriting. Most of the corpora are designed to be used in specific Arabic natural language tasks such as optical character recognition for both handwriting and printing text [10], and speech recognition [11]. Also, ARALEX online is a dataset interested in the morphology and steam of the word which is used in translation [12].…”

Section: Related Workmentioning

confidence: 99%

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

Mohamed¹,

Hussien²,

Mousa³

2021

IJITCS

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There is a massive amount of different information and data in the World Wide Web, and the number of Arabic users and contents is widely increasing. Information extraction is an essential issue to access and sort the data on the web. In this regard, information extraction becomes a challenge, especially for languages, which have a complex morphology like Arabic. Consequently, the trend today is to build a new corpus that makes the information extraction easier and more precise. This paper presents Arabic linguistically analyzed corpus, including dependency relation. The collected data includes five fields; they are a sport, religious, weather, news and biomedical. The output is CoNLL universal lattice file format (CoNLL-UL). The corpus contains an index for the sentences and their linguistic meta-data to enable quick mining and search across the corpus. This corpus has seventeenth morphological annotations and eight features based on the identification of the textual structures help to recognize and understand the grammatical characteristics of the text and perform the dependency relation. The parsing and dependency process conducted by the universal dependency model and corrected manually. The results illustrated the enhancement in the dependency relation corpus. The designed Arabic corpus helps to quickly get linguistic annotations for a text and make the information Extraction techniques easy and clear to learn. The gotten results illustrated the average enhancement in the dependency relation corpus.

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A Dataset for Speech Recognition to Support Arabic Phoneme Pronunciation

Cited by 12 publications

References 8 publications

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

An Intelligent Framework Based on Deep Learning for Online Quran Learning during Pandemic

Arabic Mispronunciation Recognition System Using LSTM Network

ADPBC: Arabic Dependency Parsing Based Corpora for Information Extraction

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