<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This paper first investigates English teaching using an AI speech recognition platform. In the feature value analysis module, using the hidden Markov model is beneficial to improve the accuracy of MFCC features for parameter speech recognition. Therefore, the hidden Markov model is proposed for English audio recognition. Secondly, according to the recognition platform pronunciation error detection, the speech pronunciation scoring algorithm is proposed, and an example analysis of English blended teaching based on AI speech recognition is conducted. The results show that the blended teaching mode is 0.33 points higher than the traditional teaching mode in the entrance examination, 2.96 points higher in the total score of Grade 4, 2.10 points higher in listening, 0.57 points higher in reading, and 0.31 points higher in writing, and the effect of blended teaching is positively correlated with the students’ entrance English level. This study proposes a blended English teaching strategy that utilizes a speech recognition platform to achieve innovation and diversification of English teaching efforts.</p>
</abstract>

This paper first investigates English teaching using an AI speech recognition platform. In the feature value analysis module, using the hidden Markov model is beneficial to improve the accuracy of MFCC features for parameter speech recognition. Therefore, the hidden Markov model is proposed for English audio recognition. Secondly, according to the recognition platform pronunciation error detection, the speech pronunciation scoring algorithm is proposed, and an example analysis of English blended teaching based on AI speech recognition is conducted. The results show that the blended teaching mode is 0.33 points higher than the traditional teaching mode in the entrance examination, 2.96 points higher in the total score of Grade 4, 2.10 points higher in listening, 0.57 points higher in reading, and 0.31 points higher in writing, and the effect of blended teaching is positively correlated with the students’ entrance English level. This study proposes a blended English teaching strategy that utilizes a speech recognition platform to achieve innovation and diversification of English teaching efforts.

5g web service based on AI speech recognition platform in blended teaching application of English in higher education

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

This paper first investigates English teaching using an AI speech recognition platform. In the feature value analysis module, using the hidden Markov model is...

5g web service based on AI speech recognition platform in blended teaching application of English in higher education

Pubblicato online: 28 ott 2023

Pagine: -

Ricevuto: 22 dic 2022

Accettato: 08 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00843

Parole chiave
AI speech recognition, MFCC, hidden Markov model, Error detection, Pronunciation scoring algorithm, Blended English teaching

© 2023 Chunyan Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

5g web service based on AI speech recognition platform in blended teaching application of English in higher education

Pubblicato online: 28 ott 2023

Pagine: -

Ricevuto: 22 dic 2022

Accettato: 08 mag 2023

DOI: https://doi.org/10.2478/amns.2023.2.00843

Parole chiaveAI speech recognition, MFCC, hidden Markov model, Error detection, Pronunciation scoring algorithm, Blended English teaching

© 2023 Chunyan Wang, published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Parole chiave
AI speech recognition, MFCC, hidden Markov model, Error detection, Pronunciation scoring algorithm, Blended English teaching