Formation of auditory and speech competences in learning English based on neural network technologies: psycholinguistic aspect

Abstract

The objective of this research is to investigate the effectiveness of integrating natural language processing (NLP) technologies into an English language learning program aimed at enhancing auditory and speaking competencies. The methodology of the research is grounded in the development and testing of the intervention effectiveness of neural network technologies apps Speechace and Rosetta Stone (based on advanced speech recognition and language modeling) for language learning within the educational process. A mixed-method approach combining statistical data analysis with qualitative surveys and testing was employed for data analysis. The results demonstrated the effectiveness of the course, assessed through pre- and post-test evaluations of students’ auditory and speaking skills, revealing significant improvements in both groups. The rate of score increase for Group 1 was calculated to be approximately 5.53%. In contrast, the rate of score increase for Group 2 was noticeably higher, at approximately 9.05%. The range of errors between the analytical value obtained using the traditional NLP algorithm and the actual value ranged from 0.008 to 0.012. This indicates that the algorithm’s predictions correspond to the exact values of cognitive processing factors with minimal error. These results hold practical implications, as program developers and educators can utilize them to substantiate their pedagogical practices and develop a more effective and engaging language learning experience. The novelty is that the study developed an English language learning model based on NLP technologies and the selected applications have not been analyzed before in the context of auditory and speech competencies.

Keywords

• Artificial intelligence
• mobile learning
• natural language processing
• neurolinguistic programming
• speech production
• Introductory Linguistics