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Article

Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback

by
Fengqin Liu
1,
Korawit Orkphol
2,
Natthapon Pannurat
2,
Thanat Sooknuan
3,
Thanin Muangpool
4,
Sanya Kuankid
4 and
Montri Phothisonothai
2,*
1
School of Foreign Languages, Dali University, Dali 671003, China
2
Department of Computer Engineering, Faculty of Engineering at Sriracha, Kasetsart University, Sriracha 20230, Thailand
3
Faculty of Engineering and Technology, Rajamangala University of Technology Isan, Nakhon Ratchasima 30000, Thailand
4
Faculty of Science and Technology, Nakhon Pathom Rajabhat University, Nakhon Pathom 73000, Thailand
*
Author to whom correspondence should be addressed.
Inventions 2025, 10(4), 46; https://doi.org/10.3390/inventions10040046
Submission received: 28 April 2025 / Revised: 31 May 2025 / Accepted: 16 June 2025 / Published: 24 June 2025
(This article belongs to the Special Issue Advances and Innovations in Deep Learning: Unveiling Multidisciplinary Applications and Challenges)
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Abstract

This paper introduces an innovative mobile-assisted language-learning (MALL) system that harnesses deep learning technology to analyze pronunciation patterns and deliver real-time, personalized feedback. Drawing inspiration from how the human brain processes speech through neural pathways, our system analyzes multiple speech features using spectrograms, mel-frequency cepstral coefficients (MFCCs), and formant frequencies in a manner that mirrors the auditory cortex’s interpretation of sound. The core of our approach utilizes a convolutional neural network (CNN) to classify pronunciation patterns from user-recorded speech. To enhance the assessment accuracy and provide nuanced feedback, we integrated a fuzzy inference system (FIS) that helps learners identify and correct specific pronunciation errors. The experimental results demonstrate that our multi-feature model achieved 82.41% to 90.52% accuracies in accent classification across diverse linguistic contexts. The user testing revealed statistically significant improvements in pronunciation skills, where learners showed a 5–20% enhancement in accuracy after using the system. The proposed MALL system offers a portable, accessible solution for language learners while establishing a foundation for future research in multilingual functionality and mobile platform optimization. By combining advanced speech analysis with intuitive feedback mechanisms, this system addresses a critical challenge in language acquisition and promotes more effective self-directed learning.
Keywords: speech recognition; accent classification; convolutional neural networks; fuzzy inference systems speech recognition; accent classification; convolutional neural networks; fuzzy inference systems

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MDPI and ACS Style

Liu, F.; Orkphol, K.; Pannurat, N.; Sooknuan, T.; Muangpool, T.; Kuankid, S.; Phothisonothai, M. Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback. Inventions 2025, 10, 46. https://doi.org/10.3390/inventions10040046

AMA Style

Liu F, Orkphol K, Pannurat N, Sooknuan T, Muangpool T, Kuankid S, Phothisonothai M. Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback. Inventions. 2025; 10(4):46. https://doi.org/10.3390/inventions10040046

Chicago/Turabian Style

Liu, Fengqin, Korawit Orkphol, Natthapon Pannurat, Thanat Sooknuan, Thanin Muangpool, Sanya Kuankid, and Montri Phothisonothai. 2025. "Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback" Inventions 10, no. 4: 46. https://doi.org/10.3390/inventions10040046

APA Style

Liu, F., Orkphol, K., Pannurat, N., Sooknuan, T., Muangpool, T., Kuankid, S., & Phothisonothai, M. (2025). Intelligent Mobile-Assisted Language Learning: A Deep Learning Approach for Pronunciation Analysis and Personalized Feedback. Inventions, 10(4), 46. https://doi.org/10.3390/inventions10040046

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