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

<p>Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.</p>
</abstract>

Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application of speech network analysis to spoken English teaching. Thus, this paper suggests a method for improving 6G technology. This paper primarily focuses on the following areas: First, to prevent overfitting, the dropout technique under 6G is added to the model, and the speech feature extracted from the data by the coding network is directly utilized. We then utilize the feature for either reconstruction or classification purposes. Initially, we perform unsupervised reconstruction using unlabeled data, and once we obtain the network parameters, we use the labeled data for classification. The next step involves feeding both data simultaneously into the model to calculate the error. Finally, we use gradient descent to minimize the error and optimize the parameters until the model converges. This model can convert sound-related symbols or information into visual symbols, and it can also convert speech into text. Comparative analysis shows that the improved method in this paper is far better than other methods in terms of noise reduction, and the signal-to-noise ratio of this paper is 3.76 dB higher than that of the LMS algorithm and 1.36 dB higher than that of the spectral subtraction method, which can stimulate the students’ interest in learning spoken English and increase the student’s interest in learning by 13.64% as a whole. It can be summarized through the comparison that this paper’s improved method increases the clarity of speech network analysis, which helps students teach spoken English.

Speech Network Analysis and Spoken English Teaching for 6G Wireless Transmission Technology

Applied Mathematics and Nonlinear Sciences

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

Recent years have seen intensive research and widespread application of 6G wireless transmission technology, providing a technical bridge for the application...

Speech Network Analysis and Spoken English Teaching for 6G Wireless Transmission Technology

Publié en ligne: 05 août 2024

Pages: -

Reçu: 28 mars 2024

Accepté: 17 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2147

Mots clés
6G wireless transmission, Dropout technology, Speech characterization, Spectral subtraction, Spoken English teaching

© 2024 Meimei Wu, published by Sciendo

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

Speech Network Analysis and Spoken English Teaching for 6G Wireless Transmission Technology

Publié en ligne: 05 août 2024

Pages: -

Reçu: 28 mars 2024

Accepté: 17 juin 2024

DOI: https://doi.org/10.2478/amns-2024-2147

Mots clés6G wireless transmission, Dropout technology, Speech characterization, Spectral subtraction, Spoken English teaching

© 2024 Meimei Wu, published by Sciendo

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

Mots clés
6G wireless transmission, Dropout technology, Speech characterization, Spectral subtraction, Spoken English teaching