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

  • Faria Nazir Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan.
  • Muhammad Nadeem Majeed Department of Software Engineering, University of Engineering and Technology Taxila, Pakistan.
  • Mustansar Ali Ghazanfar Department of Computer Science, The School of Architecture, Computing and Engineering, University of East London, London, United Kingdom.
  • Muazzam Maqsood Department of Computer Science, COMSATS Institute of Information and Technology, Islamabad, Attock Campus, Pakistan.

Abstract

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.

Published
Apr 1, 2021
How to Cite
NAZIR, Faria et al. An Arabic Mispronunciation Detection System Based on the Frequency of Mistakes for Asian Speakers. Mehran University Research Journal of Engineering and Technology, [S.l.], v. 40, n. 2, p. 279 - 297, apr. 2021. ISSN 2413-7219. Available at: <https://publications.muet.edu.pk/index.php/muetrj/article/view/2081>. Date accessed: 15 apr. 2021. doi: http://dx.doi.org/10.22581/muet1982.2102.03.
Section
Articles
This is an open Access Article published by Mehran University of Engineering and Technolgy, Jamshoro under CCBY 4.0 International License