{"id":9847,"date":"2026-08-12T14:30:52","date_gmt":"2026-08-12T06:30:52","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=9847"},"modified":"2026-08-17T23:43:54","modified_gmt":"2026-08-17T15:43:54","slug":"jase-202611-34-035","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202611-34-035","title":{"rendered":"Improving the Embedded AI Enhanced Learning Environment for College English Teaching"},"content":{"rendered":"\n
\n
\n

<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

\n

<\/i> Volume 34<\/a><\/p>\n<\/div>\n\n\n\n

\n
\n
\n
\n

Yuanyang Lei<\/i><\/a><\/p>\n\n\n\n

The Department of General Education, Henan Medical College, Zhengzhou, 450000, China.<\/p>\n<\/div>\n\n\n\n

\n

Received: April 29, 2026
Accepted: June 02, 2026
Publication Date: August 12, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

\"\u4e0a\u50b3\u5716\u7247\"\n\n\n

SS-DRNN model\u2019s Overview<\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:\u00a0 BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202611_34.035<\/a>\u00a0\u00a0<\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

<\/div>\n\n\n\n

To enhance the English learning environment for college students, this study explores the integration of Artificial Intelligence (AI), focusing on pronunciation improvement through an AI-powered system. Traditional teaching methods face challenges such as large class sizes and limited personalized feedback, which hinder effective pronunciation development. To address these limitations, an AI-driven framework is proposed to provide real-time, targeted feedback for correcting pronunciation errors. The study utilizes English speech data collected from university students with varying proficiency levels, including beginner, intermediate, and advanced learners. The dataset comprises monologues, dialogues, and pronunciation drills, capturing common errors such as misarticulations, vowel and consonant inconsistencies, and stress-related issues. The data are annotated with word-level transcriptions and error labels, along with acoustic features such as pitch, duration, formant frequencies, and Mel-frequency cepstral coefficients (MFCCs). For error detection and classification, a Salp Swarm Integrated Dense Recurrent Neural Network (SS-DRNN) model is employed. The model enables automatic identification and correction of pronunciation errors while delivering adaptive feedback. Experimental results demonstrate high performance, achieving 98.25% accuracy and strong F1-score, precision, and recall. The proposed system enhances learning efficiency, reduces teacher workload, and supports scalable, personalized English instruction in higher education environments.<\/p>\n\n\n\n

Keywords: Learning Environment, College English Teaching, Mel-Frequency Cepstral Coefficients (MFCCs), English Speech Data, Feedback.<\/em><\/p>\n\n\n\n

<\/div>\n\n\n\n
\n
\n
\n
    \n
  1. [1] R. Prabhavalkar, T. Hori, T. N. Sainath, R. Schluter, and S. Watanabe, (2024) \u201cEnd-to-End Speech Recognition: A Survey\u201d IEEE\/ACM Transactions on Audio, Speech, and Language Processing 32: 325\u2013351. DOI: 10.1109\/TASLP.2023.3328283.<\/li>\n
  2. [2] X. Li and X. Huang, (2024) \u201cImprovement and Optimization Method of College English Teaching Level Based on Convolutional Neural Network Model in an Embedded Systems Context\u201d Computer-Aided Design and Applications 21(S8): 212\u2013227. DOI: 10.14733\/cadaps.2024.S8.212-227.<\/li>\n
  3. [3] F. Wu, Y. Chen, and D. Han, (2022) \u201cDevelopment countermeasures of college English education based on deep learning and artificial intelligence\u201d Mobile Information Systems 2022: 1\u201310. DOI: 10.1155\/2022\/8389800.<\/li>\n
  4. [4] L. Huang, (2022) \u201cAn empirical study of integrating information technology in English teaching in artificial intelligence era\u201d Scientific Programming 2022: 1\u201312. DOI: 10.1155\/2022\/6775097.<\/li>\n
  5. [5] L. Geng, (2021) \u201cEvaluation model of college English multimedia teaching effect based on deep convolutional neural networks\u201d Mobile Information Systems 2021: 1\u201311. DOI: 10.1155\/2021\/1874584.<\/li>\n
  6. [6] M. N. Daoud and M. A. Ben Messaoud. \u201cPhoneme-level mispronunciation detection in Quranic recitation using Shallow Transformer\u201d. In: Proceedings of the Third Arabic Natural Language Processing Conference. 2025, 457\u2013463. DOI: 10.18653\/v1\/2025.arabicnlp-sharedtasks.63.<\/li>\n
  7. [7] H. Li, C. Tang, X. Yue, and X. Li, (2025) \u201cSentence-level consistency of conformer-based pre-training distillation for Chinese speech recognition\u201d Frontiers in Communications and Networks 6(1): 1662788\u20131662796. DOI: 10.3389\/frcmn.2025.1662788.<\/li>\n
  8. [8] K. Li, X. Qian, and H. Meng, (2016) \u201cMispronunciation detection and diagnosis in L2 English speech using multi-distribution deep neural networks\u201d IEEE\/ACM Transactions on Audio, Speech, and Language Processing 24(12): 2528\u20132539. DOI: 10.1109\/TASLP.2016.2621675.<\/li>\n
  9. [9] B. C. Yan, H. W. Wang, S. W. F. Jiang, F. A. Chao, and B. Chen. \u201cMaximum F1-score training for end-to-end mispronunciation detection and diagnosis of L2 English speech\u201d. In: IEEE International Conference on Multimedia and Expo (ICME). 2022, 1\u20136. DOI: 10.1109\/ICME52920.2022.9858931.<\/li>\n
  10. [10] H. Liu, (2021) \u201cCollege oral English teaching reform driven by big data and deep neural network technology\u201d Wireless Communications and Mobile Computing 2021: 1\u201310. DOI: 10.1155\/2021\/8389469.<\/li>\n
  11. [11] L. Peng, Y. Gao, R. Bao, Y. Li, and J. Zhang, (2023) \u201cEnd-to-End Mispronunciation Detection and Diagnosis Using Transfer Learning\u201d Applied Sciences 13(11): 6793. DOI: 10.3390\/app13116793.<\/li>\n
  12. [12] \u015e. S. \u00c7al\u0131k, A. K\u00fc\u00e7\u00fckmanisa, and Z. H. Kilimci, (2024) \u201cA novel framework for mispronunciation detection of Arabic phonemes using audio-oriented transformer models\u201d Applied Acoustics 215: 109711. DOI: 10.1016\/j.apacoust.2023.109711.<\/li>\n
  13. [13] Z. Fan, X. Zhang, M. Huang, and Z. Bu, (2024) \u201cSampleformer: An efficient conformer-based neural network for automatic speech recognition\u201d Intelligent Data Analysis 28(6): 1647\u20131659. DOI: 10.3233\/IDA-230612.<\/li>\n
  14. [14] H. Kheddar, M. Hemis, and Y. Himeur, (2024) \u201cAutomatic speech recognition using advanced deep learning approaches: A survey\u201d Information Fusion 109: 102422. DOI: 10.1016\/j.inffus.2024.102422.<\/li>\n
  15. [15] M. A. H. Wadud, M. Alatiyyah, and M. F. Mridha, (2023) \u201cNon-Autoregressive End-to-End Neural Modeling for Automatic Pronunciation Error Detection\u201d Applied Sciences 13(1): 109. DOI: 10.3390\/app13010109.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/div>\n\n\n\n

    <\/p>\n","protected":false},"author":3,"template":"wp-custom-template-detail-4-aricles","meta":{"_uag_custom_page_level_css":""},"categories":[12,1682,6],"tags":[1717],"acf":[],"uagb_featured_image_src":[],"uagb_author_info":{"display_name":"\u6797\u923a\u6db5","author_link":"\/jase\/?author=3"},"uagb_comment_info":0,"uagb_excerpt":" Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited. Download Citation:\u00a0 BibTeX | http:\/\/dx.doi.org\/10.6180\/jase.202611_34.035\u00a0\u00a0 Download PDF To enhance the English learning environment for college students, this study…","_links":{"self":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope\/9847"}],"collection":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/tkuisotope"}],"about":[{"href":"\/jase\/index.php?rest_route=\/wp\/v2\/types\/tkuisotope"}],"author":[{"embeddable":true,"href":"\/jase\/index.php?rest_route=\/wp\/v2\/users\/3"}],"wp:attachment":[{"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=9847"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=9847"},{"taxonomy":"post_tag","embeddable":true,"href":"\/jase\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=9847"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}