AI-Based English Vowel Formant Recognition and Pronunciation Correction Platform

English vowel pronunciation is very challenging for non-native English learners, as it requires precise articulation often guided by subjective feedback without objective standard. Existing tools give overall scores based on speech recognition, but lack real-time corrective feedback for articulatory details like tongue position, placement, and lip shape. They also show that there is no well-established digital standard for the measurement of English vowel pronunciation, which can hinder reliability in training for this reason. In this study, we present a pronunciation correction platform based on AI and formant analysis (F1, F2, F3) for providing real-time feedback and visualization of the articulatory adjustments. Over the course of their study, learners get feedback that gives them precise direction on tongue height, front-back location, as well as degree of lip rounding, to conform more closely to standard pronunciation. It introduces three principal innovations: (1) Fine-Grained Formant Analysis, which provides informative feedback that connects different articulatory features to very specific linguistic patterns; (2) Visualization Feedback, modeling in a visual interface the detailed deviations from target pronunciation; (3) AI-Driven Personalization, offering automated personalized feedback generated dynamically based on an individual's learning needs. This platform fills a significant gap by creating a digital standard for vowel pronunciation and enhancing pedagogy through detailed feedback as well as visualization. Its applications include, but are not limited to, language learning, speech therapy, and accent modification.

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Publication Details

Published
2024-12-20
DOI
https://doi.org/10.1109/icise-ie64355.2024.11025451
Primary Topic
Speech Recognition and Synthesis
Type
article
Field-Weighted Citation Impact
0.00

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article

AI-Based English Vowel Formant Recognition and Pronunciation Correction Platform

Jianzhi Deng, Zhengqing Zhou, C. Liu
Speech Recognition and Synthesis
article

AI-Based English Vowel Formant Recognition and Pronunciation Correction Platform

Jianzhi Deng, Zhengqing Zhou, C. Liu
article en

Abstract

English vowel pronunciation is very challenging for non-native English learners, as it requires precise articulation often guided by subjective feedback without objective standard. Existing tools give overall scores based on speech recognition, but lack real-time corrective feedback for articulatory details like tongue position, placement, and lip shape. They also show that there is no well-established digital standard for the measurement of English vowel pronunciation, which can hinder reliability in training for this reason. In this study, we present a pronunciation correction platform based on AI and formant analysis (F1, F2, F3) for providing real-time feedback and visualization of the articulatory adjustments. Over the course of their study, learners get feedback that gives them precise direction on tongue height, front-back location, as well as degree of lip rounding, to conform more closely to standard pronunciation. It introduces three principal innovations: (1) Fine-Grained Formant Analysis, which provides informative feedback that connects different articulatory features to very specific linguistic patterns; (2) Visualization Feedback, modeling in a visual interface the detailed deviations from target pronunciation; (3) AI-Driven Personalization, offering automated personalized feedback generated dynamically based on an individual's learning needs. This platform fills a significant gap by creating a digital standard for vowel pronunciation and enhancing pedagogy through detailed feedback as well as visualization. Its applications include, but are not limited to, language learning, speech therapy, and accent modification.

University of Wisconsin–Madison (US), Madison Area Technical College (US), Shenzhen University (CN)
Beijing Language and Culture University, Shenzhen University
Quality Education
Openalex Percentile: Top 72%
Speech Recognition and Synthesis
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