Mispronunciation Detection Using Deep Convolutional Neural Network Features and Transfer Learning-Based Model for Arabic Phonemes
- Submitting institution
-
University of East London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 12
- Type
- D - Journal article
- DOI
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10.1109/ACCESS.2019.2912648
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 52589
- Volume
- 7
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Technical exception
- Month of publication
- -
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
1 - Intelligent Systems
- Citation count
- 7
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Computer-assisted language learning (CALL) systems provide an automated framework to identify mispronunciation and give useful feedback. Traditionally, handcrafted acoustic-phonetic features are used to detect mispronunciation. From this line of research, this paper investigates the use of the deep convolutional neural networks for mispronunciation detection of Arabic phonemes. We empirically show that the proposed transfer learning-based method outperforms state-of-art techniques in term of accuracy. The impact of the work can be beneficial for a wider community as there has been very little work on Arabic phonemes detection using deep convolution neural networks.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -