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A Novel Natural Language Processing Model in Mobile Communication Networks

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Abstract

To improve the ability of natural language information processing in mobile communication networks, a system based on recurrent neural networks (RNN) is designed. The feedback connection is transformed into a hidden layer pointing to the input layer. According to the physiological process of human speech and the acoustic characteristics of a speech signal, a model is designed using an excitation model, channel model, and radiation model, respectively. The experimental results show that the information processing accuracy of the designed system is high and the processing time is short.

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We also declare that data availability and ethics approval is not applicable in this paper.

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Acknowledgements

The paper is supported by Research on key technologies of Chinese-English Intelligent Semantic Understanding System based on Big data with No.YB135-109.

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Authors and Affiliations

  1. School of Computer, Hubei University of Arts and Science, Xiangyang, Hubei, 441053, China

    Dan Ren

  2. Department of Math and Computer Science, Brandon University, Brandon, R7A 6A9, Canada

    Gautam Srivastava

  3. Research Centre of Interneural Computing, China Medical University, 40402, Taichung, Taiwan

    Gautam Srivastava

  4. Department of Computer Science and Mathematics, Lebanese American University, 1102, Beirut, Lebanon

    Gautam Srivastava

Authors
  1. Dan Ren
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  2. Gautam Srivastava
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Contributions

Dan Ren provided the algorithm and experimental results, wrote the manuscript, Gautam Srivastava revised the paper, supervised and analyzed the experiments.

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Correspondence to Gautam Srivastava.

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Ren, D., Srivastava, G. A Novel Natural Language Processing Model in Mobile Communication Networks. Mobile Netw Appl 27, 2575–2584 (2022). https://doi.org/10.1007/s11036-022-02072-9

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