A study of non-native accent correction techniques combining phonetics, machine learning and biomechanics
Year: 2025
Type: article
Abstract: This study provides an in-depth discussion of non-native accent correction techniques, combining phonological principles with insights from biomechanics and machine learning algorithms. By examining the physical aspects of speech production, such as articulatory movements and vocal tract dynamics, the research highlights how biomechanical factors influence the pronunciation characteristics of non-native speakers. The study reports on the current state of the art in accent correction technology, detailing how biomechanical analysis can enhance the understanding of speech patterns and contribute to more effective correction techniques. Experimental investigations verify the effectiveness of these methods across different language contexts, demonstrating significant improvements in pronunciation accuracy, fluency, and user satisfaction. By incorporating biomechanical principles, this research provides a new theoretical basis and technical support for the field of non-native accent correction, which is of positive significance for the promotion of cross-cultural communication, as they address the physical challenges faced by non-native speakers in articulating sounds specific to different languages.
DOI: 10.62617/mcb725
Author: Yanziye Wei
Institution: Lingnan University
Language: English
Subfield: Experimental and Cognitive Psychology
Field: Psychology
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4406253086