Impact of biomechanical properties of tongue muscles on accuracy of English vowel pronunciation
Abstract
Pronunciation is a complex physiological process. Traditional research usually uses static pronunciation tests and fails to observe the dynamic changes of tongue muscles during pronunciation. This paper aims to comprehensively analyze the structure and function of tongue muscles and their role in English vowel pronunciation from the perspective of tongue muscle biomechanics, and provide a systematic framework for understanding. This paper designs multiple pronunciation tasks to evaluate participants’ pronunciation accuracy and dynamic changes of tongue muscles. Through multi-modal technology, dynamic images and electromyographic signals of the tongue are synchronously acquired to analyze the precise relationship between tongue movement and muscle activity in the pronunciation of English vowels. A tongue biomechanical model is constructed based on finite element analysis and Hill model to precisely simulate the mechanical response of tongue muscle activity and tongue position changes during pronunciation. The experimental results show that there is a significant negative correlation between electromyographic activity and pronunciation quality. The closer the correlation coefficient is to −1, the higher the consistency. The tongue is positioned higher and forward during pronunciation, making it easier to control, so that the pronunciation can be more accurate with less deviation. The greater the movement and flexibility of the tongue, the better it is able to form clear vowel pronunciations. In short, the tongue muscles achieve precise control of tongue position through the coordinated action of internal and external muscles during vowel pronunciation, which is beneficial to improving pronunciation accuracy.
References
1. Sugiarto R, Prihantoro P, Edy S. THE IMPACT OF SHADOWING TECHNIQUE ON TERTIARY STUDENTS’ENGLISH PRONUNCIATION. Linguists: Journal Of Linguistics and Language Teaching. 2020; 6(1): 114-125.
2. Pourhosein Gilakjani A, Rahimy R. Using computer-assisted pronunciation teaching (CAPT) in English pronunciation instruction: A study on the impact and the Teacher’s role. Education and information technologies. 2020; 25(2): 1129-1159.
3. Tsunemoto A, McDonough K. Exploring Japanese EFL learners’ attitudes toward English pronunciation and its relationship to perceived accentedness. Language and speech. 2021; 64(1): 24-34.
4. Djurayeva YA. Enhancing English pronunciation in learning process. Academic research in educational sciences, 2021; 2(2): 302-306.
5. Pakpahan M. The Significance of English Phonology Learning Towards the Improvement of Pronunciation Accuracy of English Vowel Sounds: A Review. BLESS. 2023; 3(1): 9-19.
6. Garita Sanchez M R, Gonzalez Lutz M I, Solis Perez N. English vowel sounds: Pronunciation issues and student and faculty perceptions. Actualidades Investigativas en Educación. 2019; 19(3): 33-67.
7. Kobilova NR. Importance of pronunciation in English language communication. Academic research in educational sciences. 2022; 3(6): 592-597.
8. Floare Bora S. Taking literature off page! The effectiveness of a blended drama approach for enhancing L2 oral accuracy, pronunciation and complexity. Language Teaching Research. 2024; 28(5): 1869-1892.https://doi.org/10.1177/13621688211043490
9. Sardegna VG. Evidence in favor of a strategy-based model for English pronunciation instruction. Language Teaching. 2022; 55(3): 363-378.https://doi.org/10.1017/S0261444821000380
10. Fogarty M J, Sieck G C. Tongue muscle contractile, fatigue, and fiber type properties in rats. Journal of Applied Physiology. 2021; 131(3): 1043-1055.
11. Goto A, Kokabu S, Dusadeemeelap C, et al. Tongue muscle for the analysis of head muscle regeneration dynamics. Journal of Dental Research. 2022; 101(8): 962-971.https://doi.org/10.1177/00220345221075966
12. Szelenyi A, Fava E. Long latency responses in tongue muscle elicited by various stimulation sites in anesthetized humans–New insights into tongue-related brainstem reflexes. Brain Stimulation. 2022; 15(3): 566-575.
13. Zhu Mengxian, Jiang Chenghui, Zhou Guangchao, Wang Binbing, Li Sheng, Shi Xinghui. Research progress on the analysis of tongue-movement patterns during articulation by ultrasound imaging. International Journal of Stomatology, 2022, 49(3): 356-361.
14. Jia M, Keasuwan C. A STUDY ON THE PRONUNCIATION TEACHING OF /A/ FOR CHINESE THAI LANGUAGE LEARNERS. Chinese Language and Culture Journal. 2021; 8(1): 197-206.https://so02.tci-thaijo.org/index.php/clcjn/article/view/246756
15. Wang Tingwei, Wu Junfa, Hu Ruiping, Zhang Yunfeng, Jiang Congyu, Fan Shunjuan, et al. Feasibility study of tongue pressure meter for evaluating tongue muscle strength and endurance. Chinese Journal of Physical Medicine and Rehabilitation. 2021; 43(9): 832-834.https://doi.org/10.3760/cma.j.issn.0254-1424.2021.09.015
16. Lu L, Mao J, Wang W, Ding G, Zhang Z. A study of personal recognition method based on EMG signal. IEEE Transactions on Biomedical Circuits and Systems. 2020; 14(4): 681-691.
17. Sharma A, Sharma I, Kumar A. Signal Acquisition and Time–Frequency Perspective of EMG Signal-based Systems and Applications[J]. IETE Technical Review. 2024; 41(4): 466-485
18. Kent RD, Rountrey C. What acoustic studies tell us about vowels in developing and disordered speech. American Journal of Speech-Language Pathology. 2020; 29(3): 1749-1778.
19. Oganian Y, Bhaya-Grossman I, Johnson K, Chang E F. Vowel and formant representation in the human auditory speech cortex. Neuron. 2023; 111(13): 2105-2118.
20. Gohel V, Mehendale N. Review on electromyography signal acquisition and processing. Biophysical reviews, 2020, 12(6): 1361-1367.
21. Pakniyat N, Soundirarajan M, Gohari S, Burvill C, Krejcar O, Namazi H. Decoding of facial muscle-brain relation by information-based analysis of electromyogram (EMG) and electroencephalogram (EEG) signals. Waves in Random and Complex Media, 2024, 34(4): 3599-3608.
22. Zolkov E, Weiss R, Cohen E. Analysis and design of N-path band-pass filters with negative base band resistance. IEEE Transactions on Circuits and Systems I: Regular Papers, 2020, 67(7): 2250-2262.
23. Garcia-Martinez H, Avila-Navarro E, Torregrosa-Penalva G, Delmonte N, Silvestri L, Marconi S, et al. Design and fabrication of a band-pass filter with EBG single-ridge waveguide using additive manufacturing techniques. IEEE Transactions on Microwave Theory and Techniques, 2020, 68(10): 4361-4368.
24. Clunie D A. DICOM format and protocol standardization—a core requirement for digital pathology success. Toxicologic Pathology, 2021, 49(4): 738-749.
25. Wei Y, Luo Q, Mantooth A. Comprehensive comparisons between frequency‐domain analysis and time‐domain analysis for LLC resonant converter[J]. IET Power Electronics, 2020, 13(9): 1735-1745.
26. Shi B, Zhao Z, Zhu Y, Wang X. Time-Domain and Frequency-Domain Analysis of SiC MOSFET Switching Transients Considering Transmission of Control, Drive, and Power Pulses. IEEE Journal of Emerging and Selected Topics in Power Electronics, 2021, 9(5): 6441-6452.
27. Kong L, Nian H. Fault detection and location method for mesh-type DC microgrid using pearson correlation coefficient. IEEE Transactions on Power Delivery, 2020, 36(3): 1428-1439.
28. Peng S, Han W, Jia G. Pearson correlation and transfer entropy in the Chinese stock market with time delay. Data Science and Management, 2022, 5(3): 117-123.
29. Uerlich R, Ambikakumari Sanalkumar K, Bokelmann T, Vietor T. Finite element analysis considering packaging efficiency of innovative battery pack designs. International Journal of Crashworthiness, 2020, 25(6): 664-679.
30. Mahantesh MM, Rao K V S R, Chandra A C P, Vijayakumar M N, Nandini B, Prasad C D, et al. Design and modeling using finite element analysis for the sitting posture of computer users based on ergonomic perspective[J]. International Journal on Interactive Design and Manufacturing (IJIDeM), 2024, 18(8): 5875-5891.
31. Biltz N K, Collins K H, Shen K C, Schwartz K, Harris C A, Meyer G A. Infiltration of intramuscular adipose tissue impairs skeletal muscle contraction[J]. The Journal of physiology, 2020, 598(13): 2669-2683.
32. Viljoen S, Hanekom T, Farina D. Effect of characteristics of dynamic muscle contraction on crosstalk in surface electromyography recordings[J]. SAIEE Africa Research Journal, 2021, 98(1): 18-29.
33. Jalali Z, Noorzai E, Heidari S. Design and optimization of form and facade of an office building using the genetic algorithm[J]. Science and Technology for the Built Environment, 2020, 26(2): 128-140.
34. Ahmmed T, Akhter I, Karim S M R, Ahamed F S. Genetic algorithm based PID parameter optimization. American Journal of Intelligent Systems, 2020, 10(1): 8-13.
35. Gad A G. Particle swarm optimization algorithm and its applications: a systematic review. Archives of computational methods in engineering, 2022, 29(5): 2531-2561.
36. Zeng N, Wang Z, Liu W, Zhang H, Hone K, Liu X. A dynamic neighborhood-based switching particle swarm optimization algorithm. IEEE Transactions on Cybernetics, 2020, 52(9): 9290-9301.
37. Qin Lijuan, Feng Naiqin. Sparse Data Feature Extraction of Sparse Data Based on Deep Learning Back Propagation[J]. Computer Simulation, 2022, 39(5): 333-336.
38. Hongwei GUO, Ce ZHU, Xu YANG, Lei LUO. Temporal dependent rate-distortion optimization based on distortion backward propagation. Journal on Communications, 2022, 43(12): 222-232.https://doi.org/10.11959/j.issn.1000-436x.2022230
Copyright (c) 2025 Ping Zhang, Xiaoguang Chen
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.