AI-Integrated Computational Framework for Pronunciation Error Analysis Through Native–Learner Speech Comparison
Year: 2026
Type: article
Abstract: This study proposes an AI-integrated computational framework for pronunciation error analysis through native–learner speech comparison. The framework integrates Whisper-based Automatic Speech Recognition (ASR), acoustic feature extraction, statistical analysis, and machine learning to analyze learner pronunciation. Unlike previous studies that primarily rely on individual transcription-based or acoustic measures, the proposed framework integrates multiple pronunciation indicators into a composite Pronunciation Accuracy Score (PAS) formulated in this study. The framework was evaluated using a dataset comprising 45 non-native English learners and one native English speaker. PAS combines Pronunciation Error Rate (PER), Word Error Rate (WER), Pitch Similarity, Rhythm Similarity, and Mel-Frequency Cepstral Coefficient (MFCC) Similarity. Pearson correlation analysis showed that Rhythm Similarity (r = 0.544) and Pitch Similarity (r = 0.510) exhibited the strongest positive correlations with PAS, whereas Word Error Rate showed the strongest negative correlation (r = −0.495). K-Means clustering identified three learner groups with distinct pronunciation characteristics within the tested dataset. Although evaluated on a relatively small dataset without validation against expert human ratings, the proposed framework provides a promising foundation for Computer-Assisted Pronunciation Training (CAPT) and future multilingual pronunciation assessment.
Institution: Universitas Indo Global Mandiri
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
SDG: Quality education
Open Access status: gold
Estimated APC paid: $32
OpenAlex ID: W7223457433