{"id":3836,"date":"2026-04-25T23:02:16","date_gmt":"2026-04-25T15:02:16","guid":{"rendered":"\/jase\/?post_type=tkuisotope&p=3836"},"modified":"2026-04-26T20:07:09","modified_gmt":"2026-04-26T12:07:09","slug":"jase-202609-32-018","status":"publish","type":"tkuisotope","link":"\/jase\/?tkuisotope=jase-202609-32-018","title":{"rendered":"A Method for Improving the Quality of Broadcasting and Hosting Teaching Based on Deep Learning"},"content":{"rendered":"\n
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<\/i> 2026<\/a><\/p>\n<\/div>\n\n\n\n

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<\/i> Volume 32<\/a><\/p>\n<\/div>\n\n\n\n

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Xiaoyan Zhang<\/i><\/a><\/p>\n\n\n\n

Nanning College of Technology, Nanning 530000, Guangxi, China<\/p>\n<\/div>\n\n\n\n

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Received: February 22, 2026
Accepted: March 27, 2026
Publication Date: April 25, 2026<\/p>\n<\/div>\n<\/div>\n\n\n\n

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Change in loss and accuracy during model training <\/p>\n<\/div>\n<\/div>\n\n\n\n

<\/i> Copyright <\/strong>The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0)<\/a>, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.<\/p>\n\n\n\n

Download Citation:  BibTeX <\/a>| http:\/\/dx.doi.org\/10.6180\/jase.202609_32.018<\/a>  <\/p>\n\n\n\n

Download PDF<\/a><\/p>\n\n\n\n

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This study tackles challenges in evaluating and improving broadcasting and hosting teaching by proposing an intelligent analysis method using deep learning models. It collects multisource data like speech, text, and feedback to build a multi-dimensional feature system. Combining LSTM networks with natural language processing, a predictive model for automatic expression quality scoring is designed. The system tracks individual performance, generating structured evaluations and personalized improvement suggestions. The model showed good accuracy and stability during experiments, and practical tests demonstrated significant student improvements in speech rate, emotional expression, and coherence. Teachers gave positive feedback on the system\u2019s support for teaching. The study enhances personalized teaching strategies and shows that deep learning-based analysis has strong adaptability and potential for broadcasting and hosting courses.<\/p>\n\n\n\n

Keywords: broadcasting and hosting; teaching quality; deep learning; LSTM network; natural language processing technology<\/em><\/p>\n\n\n\n

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