MARC

LEADER 00000nab a2200000uu 4500
001 3205436423
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022 |a 1548-1093 
022 |a 1548-1107 
024 7 |a 10.4018/IJWLTT.377130  |2 doi 
035 |a 3205436423 
045 2 |b d20250101  |b d20251231 
100 1 |a Tang, Nan  |u Foreign Language College, Zhengzhou Normal University, China 
245 1 |a Enhancing Oral English Proficiency Through Human-Computer Interaction 
260 |b IGI Global  |c 2025 
513 |a Journal Article 
520 3 |a Human-Machine Interaction (HMI) technology has revolutionized the landscape of oral English education, offering new possibilities for improving learning efficiency and experiences. This paper presents an innovative teaching system that integrates real-time speech recognition and feedback capabilities with advanced natural language processing (NLP) and machine learning algorithms. The system is designed to provide personalized learning paths based on learners' performance data, ensuring tailored resources and guidance. Emphasizing user experience and interactive design, it aims to stimulate learner interest and motivation. Research findings indicate significant improvements in students' pronunciation, fluency, and grammar, alongside high levels of user satisfaction. However, challenges remain in fully replicating genuine human interactions and addressing technical limitations. Future work will focus on enhancing conversational abilities, personalization, and multimodal feedback mechanisms to better prepare students for real-world communication scenarios. 
653 |a Teaching 
653 |a Speech recognition 
653 |a Students 
653 |a Language proficiency 
653 |a Human technology relationship 
653 |a Feedback 
653 |a Conversation 
653 |a Human-computer interaction 
653 |a Machine learning 
653 |a Technology 
653 |a Comparative studies 
653 |a Hypotheses 
653 |a Motivation 
653 |a Voice recognition 
653 |a Personalized learning 
653 |a Algorithms 
653 |a Learning 
653 |a Real time 
653 |a Speech 
653 |a English language 
653 |a Pronunciation 
653 |a Education 
653 |a Customization 
653 |a Language acquisition 
653 |a Fluency 
653 |a User experience 
653 |a Competence 
653 |a Grammar 
653 |a Educational objectives 
653 |a User satisfaction 
653 |a English proficiency 
653 |a Language instruction 
653 |a Natural language processing 
653 |a Design 
653 |a Ability 
653 |a Satisfaction 
653 |a Oral Language 
653 |a Literature Reviews 
653 |a Comparative Analysis 
653 |a Language Processing 
653 |a Educational Environment 
653 |a Learner Engagement 
653 |a English 
653 |a Language Role 
653 |a Language Usage 
653 |a Guidance 
653 |a Educational Resources 
653 |a Influence of Technology 
653 |a Learning Processes 
653 |a Computers 
653 |a Learning Experience 
653 |a Learning Theories 
653 |a Intelligence 
653 |a Oral English 
653 |a Educational Experience 
653 |a Educational Facilities Improvement 
773 0 |t International Journal of Web-Based Learning and Teaching Technologies  |g vol. 20, no. 1 (2025), p. 1-19 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3205436423/abstract/embedded/6A8EOT78XXH2IG52?source=fedsrch 
856 4 0 |3 Full Text - PDF  |u https://www.proquest.com/docview/3205436423/fulltextPDF/embedded/6A8EOT78XXH2IG52?source=fedsrch