Multifaceted features of GMM-based English pronunciation learning systems in Chinese culture course design

Advancements in computer technology and speech signal processing have driven the development of English pronunciation learning systems, particularly in applications involving artificial intelligence and remote education. This study focuses on a text-dependent English pronunciation learning system that leverages the Gaussian Mixture Model (GMM) for precise speaker identification and pronunciation improvement. By extracting speech features using advanced techniques such as Fast Fourier Transform (FFT), Cepstrum (CEPST), and Mel Frequency Cepstral Coefficients (MFCC), the system captures perceptually relevant characteristics of speech, mirroring human auditory processing. The GMM algorithm uses these features to construct robust speaker models, with the Maximum A Posteriori (MAP) probability method applied to match test speech data with stored models, achieving a high similarity rate exceeding 93%. The system integrates iterative learning, employing multi-stage voice recording, teacher feedback, and self-assessment to guide students in refining their pronunciation. Experimental results show that optimizing Gaussian components and feature dimensions significantly enhances accuracy. This paper emphasizes the system’s role in providing objective, data-driven evaluations, enabling teachers to offer precise feedback while empowering students with tools for independent learning. Additionally, the study explores the integration of this technology into Chinese culture courses, showcasing its interdisciplinary potential to transform language learning and cultural education.