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Secure speech-recognition data transfer in the internet of things using a power system and a tried-and-true key generation technique

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Abstract

To secure the privacy, confidentiality, and integrity of Speech Data (SD), the concept of secure Speech Recognition (SR) involves accurately recording and comprehending spoken language while employing diverse security processes. As the Internet of Things (IoT) rapidly evolves, the integration of SR capabilities into IoT devices gains significance. However, ensuring the security and privacy of private SD post-integration remains a critical concern. Despite the potential benefits, implementing the proposed Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR and integrating it with IoT devices may encounter complexities due to diverse device types. Moreover, the challenge of maintaining data security and privacy for assigned SD in practical IoT settings poses a significant hurdle. Ensuring seamless interoperability and robust security measures is essential. We introduce the Reptile Search Optimized Hidden Markov Model (RSO-HMM) for SR, utilizing retrieved aspects as speech data. Gathering a diverse range of SD from speakers with varying linguistic backgrounds enhances the accuracy of the SR system. Preprocessing involves Z-score normalization for robustness and mitigation of outlier effects. The Perceptual Linear Prediction (PLP) technique facilitates efficient extraction of essential acoustic data from speech sources. Addressing data security, Elliptic Curve Cryptography (ECC) is employed for encryption, particularly suited for resource-constrained scenarios. Our study evaluates the SR system, employing key performance metrics including accuracy, precision, recall, and F1 score. The thorough assessment demonstrates the system's remarkable performance, achieving an impressive accuracy of 96%. The primary objective revolves around appraising the system's capacity and dependability in accurately transcribing speech signals. By proposing a comprehensive approach that combines the RSO-HMM for SR, data preprocessing techniques, and ECC encryption, this study advocates for the wider adoption of SR technology within the IoT ecosystem. By tackling critical data security concerns, this approach paves the way for a safer and more efficient globally interconnected society, encouraging the broader utilization of SR technology in various applications.

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Acknowledgements

Key information project of China Southern Power Grid Energy Research Institute Power planning basic database v1.0 construction project of China Southern Power Grid (0006200000081599).

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Authors and Affiliations

  1. Energy Research Institute of China Southern Power Grid Company Limited, Guangzhou, 510663, Guangdong, China

    Zhe Wang & Shuangbai He

  2. Hainan Power Grid Co., Ltd., Sanya Power Supply Bureau, Sanya, 572099, Hainan, China

    Guoan Li

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  1. Zhe Wang
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All authors contributed to the study conception and design.All authors read and approved the final manuscript

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Correspondence to Zhe Wang.

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Wang, Z., He, S. & Li, G. Secure speech-recognition data transfer in the internet of things using a power system and a tried-and-true key generation technique. Cluster Comput 27, 14669–14684 (2024). https://doi.org/10.1007/s10586-024-04649-3

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