Modeling English vocabulary acquisition through the biomechanics of speech and Large Language Models
Abstract
This study investigates the relationship between biomechanical constraints in speech production and English vocabulary acquisition by integrating Large Language Models (LLMs). Using a sample of 51 Mandarin Chinese speakers in Shenzhen, China, divided into three age groups (children: 8–12 years, adolescents: 13–17 years, and adults: 18–25 years), we conducted a 12-week longitudinal study combining articulatory measurements with computational analysis. The research employed electromagnetic articulography, surface electromyography, and advanced language modeling to examine speech patterns and learning outcomes. Results reveal significant age-related differences in articulatory kinematics, with children showing larger tongue displacements (14.3 ± 2.1 mm) and higher muscle activation levels than adults. Integrating biomechanical constraints into LLM analysis improved prediction accuracy by 18.7% for children and 14.2% for adults, though at the cost of increased computational resources. Strong negative correlations were found between articulatory effort and learning success (r = −0.824 for children, p < 0.001), with retention rates significantly influenced by motor complexity. These findings suggest that biomechanical factors play a crucial role in vocabulary acquisition, particularly in younger learners, and that incorporating these constraints into computational models can enhance our understanding of language learning processes. This integrated approach offers new insights for developing age-appropriate language teaching methodologies and improving predictive models for learning outcomes.
References
1. Hummel, K. M. (2021). Introducing second language acquisition: Perspectives and practices.
2. Uy, F., Cojuangco, F., Canes, R. M., Kilag, O. K., Abendan, C. F., & Dicdiquin, I. (2023). Syntax and Beyond Investigating Chomsky’s Universal Grammar in the Acquisition of Second Languages. Excellencia: International Multi-disciplinary Journal of Education (2994-9521), 1(5), 345-357.
3. SWARGIARY, K. (2024). Language and Learning: The Crucial Role of Language in the Teaching-Learning Process. GOOGLE.
4. Grishechko, E. G. (2023). Language and cognition behind simile construction: A Python-powered corpus research. TLC Journal, 7(2).
5. Ciccarelli, M., Papetti, A., & Germani, M. (2023). Exploring how new industrial paradigms affect the workforce: A literature review of Operator 4.0. Journal of Manufacturing Systems, 70, 464-483.
6. Maassen, B., & Terband, H. (2024). Toward process-oriented, dimensional approaches for diagnosis and treatment of speech sound disorders in children: Position statement and future perspectives. Journal of Speech, Language, and Hearing Research, 1-22.
7. Xi, Z., Chen, W., Guo, X., He, W., Ding, Y., Hong, B., ... & Gui, T. (2023). The rise and potential of large language model based agents: A survey. arXiv preprint arXiv:2309.07864.
8. Ovy, E. G., Romanyk, D. L., Flores Mir, C., & Westover, L. (2022). Modeling and evaluating periodontal ligament mechanical behaviour and properties: A scoping review of current approaches and limitations. Orthodontics & Craniofacial Research, 25(2), 199-211.
9. Skinner, B. F. (1957). Verbal behavior. Appleton-Century-Crofts.
10. Chomsky, N. (1959). Review of Verbal Behavior by B.F. Skinner. Language, 35(1), 26-58.
11. Krashen, S. (1982). Principles and practice in second language acquisition. Pergamon Press.
12. Nation, I. S. P. (1990). Teaching and learning vocabulary. Heinle & Heinle.
13. Browman, C. P., & Goldstein, L. (1992). Articulatory phonology: An overview. Phonetica, 49(3-4), 155-180.
14. Gick, B., Wilson, I., & Derrick, D. (2013). Articulatory phonetics. John Wiley & Sons.
15. Sengan Sudhakar and S. Chenthur Pandian, (2016), ‘Hybrid Cluster-based Geographical Routing Protocol to Mitigate Malicious Nodes in Mobile Ad Hoc Network, InderScience-International Journal of Ad Hoc and Ubiquitous Computing, vol. 21, no. 4, pp. 224-236. DOI:10.1504/IJAHUC.2016.076358.
16. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.
17. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.
18. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.
19. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.
20. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.
21. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.
22. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.
23. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.
24. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors, and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030.
25. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.
26. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563-574, https://doi.org/10.53759/7669/jmc202404054.
27. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x
28. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.
29. Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. Proceedings of NAACL-HLT 2019, 4171-4186.
30. Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners—arXiv preprint arXiv:2005.14165.
31. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30.
32. Abdelhadi, A. (2022). English Articulatory Phonetics.
33. Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41(4), 1149-1160.
Copyright (c) 2025 Jingya Shang
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.
Submit a Paper
