Pronunciation errors of non-Arab learners of Arabic language

Alsulaiman, Mansour; Ali, Zulfiqar; Muhammad, Ghulam; Hindi, Afnan Al; Alfakih, Taha; Obeidat, Hussein; Alkahtani, Saad

doi:10.1109/i4ct.2014.6914189

Cited by 3 publications

(3 citation statements)

References 9 publications

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“…Fig 2 shows confusing pairs of Arabic mistaken by the Pakistani national. Sulaiman et al [40] discovered Arabic phonemes mispronounced by Pakistani nationals and also found which phoneme sound is replaced or substituted by the other phonemes to provide confusing phoneme pairs. When we take mispronunciation detection as a classification task, we have to train a separate classifier for each confusing pair that needs a lot of memory and training time.…”

Section: Grouping Of Phonemesmentioning

confidence: 99%

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An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

Nazir

Majeed

Ghazanfar

et al. 2021

Mehran Univ. res. j. eng. technol.

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Over the last few decades, the field of artificial intelligence and machine learning has evolved. Due to the advancement in these fields, much work has been done to assist language learning with the help of computers called Computer-Assisted Language Learning (CALL). Mispronunciation detection is one of the significant tasks of the CALL system. An efficient mispronunciation detection model has a positive impact on the life of second language learners by providing phoneme level feedback. In this paper, we introduce the phone grouping technique for mispronunciation detection that is based on mistakes probability. We consider mispronunciation detection as a classification problem, traditionally for this purpose, a separate classifier is trained for each phoneme mistake that requires a lot of memory and time. Instead of training a separate classifier, we group the phoneme based on their mistakes probability that helps in reducing the number of the classifiers to be trained and also saves memory and time. We use the Support Vector Machine (SVM) classifier and test the results on the Arabic dataset (28 Phonemes). The performance of our proposed method is evaluated by using accuracy. The results of the model are evaluated using the confusion matrix and gives an accuracy of 88%. Our approach outperforms the existing systems developed for Arabic phonemes in terms of accuracy and is also time/memory efficient.

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Section: Grouping Of Phonemesmentioning

confidence: 99%

“…To make efficient use of memory and time, we group the phonemes into two main groups. These groups are based on the pronunciation errors made by Pakistani nationals [40]. The phonemes with a high probability of mistakes are placed in Group1 and phonemes that have a low probability of mistakes placed in Group2.…”

Section: Grouping Of Phonemesmentioning

confidence: 99%

An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

Nazir

Majeed

Ghazanfar

et al. 2021

Mehran Univ. res. j. eng. technol.

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“…Generally, errors are committed during pronunciation due to two factors, firstly, some sounds are missing from the first language of the learners. Secondly, some sounds origin at similar points of articulations, which caused the pronunciation of one letter to be like another letter pronunciation [7].…”

Section: Introductionmentioning

confidence: 99%

Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach

et al. 2022

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Reading Quran for non-Arab is a challenge due to different mother tongues. learning Quran face-to-face is considered time-consuming. The correct pronunciation of Makhraj and Sifaat are the two things that are considered difficult. In this paper, Sifaat evaluation system was developed, focusing on Sifaat with opposites for teaching the pronunciation of the Quranic letters. A classifier-based approach has been designed for evaluating the Sifaat with opposites, using machine learning technique; the k-nearest neighbour (KNN), the ensemble random undersampling (RUSBoosted), and the support vector machine (SVM). Five separated classifiers were designed to classify the Quranic letters according to group of Sifaat with opposites, where letters that are classified to the wrong groups are considered mispronounced. The paper started with identifying the acoustic features to represent each group of Sifaat. Then, the classification method was identified to be used with each group of Sifaat, where best models were selected relying on various metrics; accuracy, recall, precision, and F-score. Cross-validation scheme was then used to protect against overfitting and estimate an unbiased generalization performance. Various acoustic features and classification models were investigated, however, only the outperformed models are reported in this paper. The results showed a good performance for the five classification models.

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Modern Standard Arabic Speech Corpora: A Systematic Review

et al. 2023

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Speech processing applications have become integral components across various domains of modern life. The design and preparation of a reliable recognition system rely heavily on the availability of suitable speech databases. While numerous speech databases exist for English and other languages, the availability of comprehensive resources for Arabic language remains limited. In light of this, we conducted a systematic review aiming to identify, analyse, and classify existing Modern Standard Arabic speech databases. Through our review, we identified 27 publicly available databases and analysed an additional 80 subjective databases. These databases were thoroughly studied, classified based on their characteristics, and subjected to a detailed analysis of research trends in the field. This paper provides a comprehensive discussion on the diverse speech databases developed for various speech processing applications. It sheds light on the purposes and unique characteristics of Arabic speech databases, enabling researchers to easily access suitable resources for their specific applications. The findings of this review contribute to bridging the gap in available Arabic speech databases and serve as a valuable resource for researchers in the field.

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Pronunciation errors of non-Arab learners of Arabic language

Cited by 3 publications

References 9 publications

An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers

Characteristics with opposite of quranic letters mispronunciation detection: a classifier-based approach

Modern Standard Arabic Speech Corpora: A Systematic Review

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