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<p>To verify the feasibility of robust speech recognition based on deep learning in sports game review. In this paper, a robust speech recognition model is built based on the generative adversarial network GAN algorithm according to the deep learning model. And the loss function, optimization function and noise reduction front-end are introduced in the model to achieve the optimization of speech extraction features through denoising process to ensure that accurate speech review data can be derived even in the game scene under noisy environment. Finally, the experiments are conducted to verify the four directions of the model algorithm by comparing the speech features MFCC, FBANK and WAVE. The experimental results show that the speech recognition model trained by the GSDNet model algorithm can reach 89% accuracy, 56.24% reduction of auxiliary speech recognition word error rate, 92.61% accuracy of speech feature extraction, about 62.19% reduction of training sample data volume, and 94.75% improvement of speech recognition performance in the speech recognition task under noisy environment. It shows that the robust speech recognition based on deep learning can be applied to sports game reviews, and also can provide accurate voice review information from the noisy sports game scene, and also broaden the application area for deep learning models.</p>
</abstract>

To verify the feasibility of robust speech recognition based on deep learning in sports game review. In this paper, a robust speech recognition model is built based on the generative adversarial network GAN algorithm according to the deep learning model. And the loss function, optimization function and noise reduction front-end are introduced in the model to achieve the optimization of speech extraction features through denoising process to ensure that accurate speech review data can be derived even in the game scene under noisy environment. Finally, the experiments are conducted to verify the four directions of the model algorithm by comparing the speech features MFCC, FBANK and WAVE. The experimental results show that the speech recognition model trained by the GSDNet model algorithm can reach 89% accuracy, 56.24% reduction of auxiliary speech recognition word error rate, 92.61% accuracy of speech feature extraction, about 62.19% reduction of training sample data volume, and 94.75% improvement of speech recognition performance in the speech recognition task under noisy environment. It shows that the robust speech recognition based on deep learning can be applied to sports game reviews, and also can provide accurate voice review information from the noisy sports game scene, and also broaden the application area for deep learning models.

Robust speech recognition based on deep learning for sports game review

Sport Art College of Guangzhou Sports University

Applied Mathematics and Nonlinear Sciences

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

{"article-title":"Robust speech recognition based on deep learning for sports game review"}

To verify the feasibility of robust speech recognition based on deep learning in sports game review. In this paper, a robust speech recognition model is built...

Robust speech recognition based on deep learning for sports game review

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 06 avr. 2022

Accepté: 07 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00075

Mots clés
Deep learning model, Generative adversarial network GAN algorithm, Robust speech recognition, Speech feature vector, Loss function

© 2023 Min Liu et al., published by Sciendo

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

Robust speech recognition based on deep learning for sports game review

Publié en ligne: 28 avr. 2023

Pages: -

Reçu: 06 avr. 2022

Accepté: 07 sept. 2022

DOI: https://doi.org/10.2478/amns.2023.1.00075

Mots clésDeep learning model, Generative adversarial network GAN algorithm, Robust speech recognition, Speech feature vector, Loss function

© 2023 Min Liu et al., published by Sciendo

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

Mots clés
Deep learning model, Generative adversarial network GAN algorithm, Robust speech recognition, Speech feature vector, Loss function