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

<p>Because the English vocabulary of water resources majors is difficult to understand and awkward to speak, this paper constructs a teaching model combined with pronunciation error detection. With the help of the feature engineering method, we extracted the pronunciation features of English for water resources majors and then used a smooth LSTM network to learn and recognize the pronunciation features so as to detect the errors in pronunciation habits and complete the correction of students’ professional English pronunciation. The pronunciation error detection rate reflects the effect of error detection, and the feasibility of the teaching model is verified according to the mastery degree of specialized vocabulary. The post-test mean value of the experimental class is 76.18 points, and the mean value of the control class is 71.12 points, which is 5.06 points higher than that of the experimental class. From the results of the independent samples t-test, the Sig value is 0.102, which is greater than 0.05, indicating that there is a significant difference in the post-test scores of the two classes. This study has a significant impact on students’ mastery of English vocabulary related to water resources.</p>
</abstract>

Because the English vocabulary of water resources majors is difficult to understand and awkward to speak, this paper constructs a teaching model combined with pronunciation error detection. With the help of the feature engineering method, we extracted the pronunciation features of English for water resources majors and then used a smooth LSTM network to learn and recognize the pronunciation features so as to detect the errors in pronunciation habits and complete the correction of students’ professional English pronunciation. The pronunciation error detection rate reflects the effect of error detection, and the feasibility of the teaching model is verified according to the mastery degree of specialized vocabulary. The post-test mean value of the experimental class is 76.18 points, and the mean value of the control class is 71.12 points, which is 5.06 points higher than that of the experimental class. From the results of the independent samples t-test, the Sig value is 0.102, which is greater than 0.05, indicating that there is a significant difference in the post-test scores of the two classes. This study has a significant impact on students’ mastery of English vocabulary related to water resources.

Innovative Design of English Teaching Mode for Water Resources Majors Based on Smoothed Long and Short-Term Memory Network Models

Applied Mathematics and Nonlinear Sciences

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

Because the English vocabulary of water resources majors is difficult to understand and awkward to speak, this paper constructs a teaching model combined with...

Innovative Design of English Teaching Mode for Water Resources Majors Based on Smoothed Long and Short-Term Memory Network Models

Publicado en línea: 11 nov 2023

Páginas: -

Recibido: 15 feb 2023

Aceptado: 22 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01079

Palabras clave
Feature engineering, LSTM networks, Articulatory features, Water resources majors, English language teaching

© 2023 Qi Li, published by Sciendo

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

Innovative Design of English Teaching Mode for Water Resources Majors Based on Smoothed Long and Short-Term Memory Network Models

Publicado en línea: 11 nov 2023

Páginas: -

Recibido: 15 feb 2023

Aceptado: 22 may 2023

DOI: https://doi.org/10.2478/amns.2023.2.01079

Palabras claveFeature engineering, LSTM networks, Articulatory features, Water resources majors, English language teaching

© 2023 Qi Li, published by Sciendo

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

Palabras clave
Feature engineering, LSTM networks, Articulatory features, Water resources majors, English language teaching