Jiawen Yu1,2This email address is being protected from spambots. You need JavaScript enabled to view it. and Baoyu Qiu3This email address is being protected from spambots. You need JavaScript enabled to view it.
1School of Foreign Languages, Nanfang College Guangzhou, Guangzhou, Guangdong, 510970, China
2Faculty of Education, Language, Psychology, and Music, SEGi University, Petaling Jaya, Selangor, 47810, Malaysia
3The Department of Communication Faculty of Social Sciences, University of Macau
Received: September 1, 2025 Accepted: October 13, 2025 Publication Date: November 12, 2025
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.
The role of artificial intelligence (AI) technology on the self-efficacy of English language learners in mastering the English language speech. The swift technological advancement has made AI-based English learning technologies, such as speech recognition, speech correction, and speaking assessment, a critical variable in improving phonological skills of learners. The present paper discusses the different roles of AI in second language learning with emphasis on the impact it has on communication preparedness and communication anxiety of learners and learning environment. It achieves this by analyzing the existing AI based learning applications and how they are developed in the English speaking test assessment systems and the digital textbook applications. Research states that AI technologies can enhance the willingness of learners to communicate, decrease the fear of communication, and improve the conditions under which language learning takes place, which all result into boosting self-efficacy among English language learners. Two items that can illustrate the technical and application limits of the existing AI technologies that need research and development are the accuracy of the speech recognition and the optimization of the learning environment. This study introduces a way to improve the performance of the HMM model in speech recognition by introducing a nonparametric approach to maximizing the duration distribution, and experimentally validates the approach on NAO robot. The results indicate that the enhanced model is the one with a significant increase in the speech recognition ability. The research represented in this paper provides new insights and perspectives of research in the related fields, as well as theoretical and practical evidence of AI application in English speech recognition.
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