Innovative approaches to English pronunciation instruction in ESL environments: integration of multi-sensor detection and advanced algorithmic feedback

Teaching English pronunciation in an ESL environment involves tailored strategies to help learners accurately produce sounds, intonation, and rhythm. This study presents an innovative method for teaching English pronunciation in an English as a Second Language environment, utilizing advanced technol...

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Vydáno v:Interactive learning environments Ročník 33; číslo 8; s. 5004 - 5024
Hlavní autoři: Ping, Li, Tao, Ning
Médium: Journal Article
Jazyk:angličtina
Vydáno: Abingdon Routledge 14.09.2025
Taylor & Francis Ltd
Témata:
Accuracy
Algorithms
Data collection
Discrepancies
Educational Technology
English (Second Language)
English as a second language
English as a second language instruction
English language
English pronunciation
Extraction
Feature extraction
Feedback
feedback neural network
Fluency
Innovations
Intonation
Neural networks
Normalization
Pronunciation
Pronunciation accuracy
Pronunciation instruction
Rhythm
Signal quality
Speech
Speech recognition
speech signal processing
Teaching
teaching evaluation
Teaching methods
ISSN:1049-4820, 1744-5191
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Shrnutí:Teaching English pronunciation in an ESL environment involves tailored strategies to help learners accurately produce sounds, intonation, and rhythm. This study presents an innovative method for teaching English pronunciation in an English as a Second Language environment, utilizing advanced technology and algorithms to enhance pronunciation accuracy, fluency, and completeness. The approach employs multi-sensor detection methods for precise data collection, preprocessing techniques such as pre-emphasis, normalization, framing, windowing, and endpoint detection to ensure high-quality speech signals. Feature extraction focuses on key attributes of pronunciation, which are then fused through a feedback neural network for comprehensive evaluation. Empirical results demonstrate significant improvements over existing methods. The proposed method achieved a teaching evaluation accuracy of 99.3%, compared to 68.9% and 77.8% for other referenced methods. Additionally, students showed higher levels of fluency, with most achieving a level of 4 or above, whereas traditional methods resulted in lower fluency levels. Spectral feature analysis indicated that the amplitude of speech signals obtained using the proposed method closely matched the original signals, unlike the discrepancies found in previous methods.
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ISSN:1049-4820
1744-5191
DOI:10.1080/10494820.2025.2476718