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

<p>Multimedia speech labs have been greatly developed in English teaching in recent years to minimize the laboratory background noise in speech signals. In this paper, from analyzing and improving the dynamic noise reduction model of the original system, two noise removal schemes, wavelet analysis and Wiener filtering are studied more systematically, and a speech enhancement model, wavelet domain Wiener filtering noise reduction model (WWF), is proposed and initially implemented by fusing the two. This model can better meet the requirements of the original system, thus laying a better foundation for subsequent speech recognition. Finally, the algorithm is used to optimize the multimedia all-digital language learning system and test this multimedia technology’s effectiveness for college English listening teaching. In the two listening tests, the average score of the experimental class in the first test increased by 6.1 points compared to the first test score, while the control class only increased by 1.8 points, and the average score of the experimental class in the second test increased by 12.9%, while the control class increased by 6.7%. Therefore, combining multimedia and foreign language teaching can make teaching activities integrate text, sound, image, image, and animation, which can mobilize and stimulate students’ learning enthusiasm and initiative to the maximum extent and thus improve teaching efficiency.</p>
</abstract>

Multimedia speech labs have been greatly developed in English teaching in recent years to minimize the laboratory background noise in speech signals. In this paper, from analyzing and improving the dynamic noise reduction model of the original system, two noise removal schemes, wavelet analysis and Wiener filtering are studied more systematically, and a speech enhancement model, wavelet domain Wiener filtering noise reduction model (WWF), is proposed and initially implemented by fusing the two. This model can better meet the requirements of the original system, thus laying a better foundation for subsequent speech recognition. Finally, the algorithm is used to optimize the multimedia all-digital language learning system and test this multimedia technology’s effectiveness for college English listening teaching. In the two listening tests, the average score of the experimental class in the first test increased by 6.1 points compared to the first test score, while the control class only increased by 1.8 points, and the average score of the experimental class in the second test increased by 12.9%, while the control class increased by 6.7%. Therefore, combining multimedia and foreign language teaching can make teaching activities integrate text, sound, image, image, and animation, which can mobilize and stimulate students’ learning enthusiasm and initiative to the maximum extent and thus improve teaching efficiency.

Analysis of the practice in college English listening teaching based on multimedia technology

Applied Mathematics and Nonlinear Sciences

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

Multimedia speech labs have been greatly developed in English teaching in recent years to minimize the laboratory background noise in speech signals. In this...

Analysis of the practice in college English listening teaching based on multimedia technology

Publié en ligne: 18 sept. 2023

Pages: -

Reçu: 03 déc. 2022

Accepté: 29 mars 2023

DOI: https://doi.org/10.2478/amns.2023.2.00342

Mots clés
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement

© 2023 Ling Wang, published by Sciendo

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

Analysis of the practice in college English listening teaching based on multimedia technology

Publié en ligne: 18 sept. 2023

Pages: -

Reçu: 03 déc. 2022

Accepté: 29 mars 2023

DOI: https://doi.org/10.2478/amns.2023.2.00342

Mots clésSpeech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement

© 2023 Ling Wang, published by Sciendo

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

Mots clés
Speech recognition, Multimedia technology, Wavelet noise reduction, Wiener filtering, Speech enhancement