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International Conference on Machine Learning for Cyber Security

ML4CS 2022: Machine Learning for Cyber Security pp 422–435Cite as

Short Speech Key Generation Technology Based on Deep Learning

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13656))

Abstract

With the increasing popularity of biometric identity authentication in important key authentication applications, biometric key generation technology has attracted more and more attention. How to extract secure, stable and high-strength keys from biometrics has become a key research direction of biometric key generation technology. However, the proposed biometric key generation technology can not meet the security requirements as a key, that is, the key is easy to be cracked, resulting in unpredictable consequences. This paper presents a framework of voiceprint key generation based on deep neural network, which is mainly composed of voiceprint feature extraction model and fuzzy extraction model. The voiceprint feature extraction model is based on the deep neural network, and the sigmoid layer is added at the end of the neural network. This operation makes the features extracted by the neural network have high accuracy and low error recognition rate after binary quantization. The voiceprint database used in this paper is aishell-wakeup-1 wakeup word database. The voiceprint key generation model proposed in this paper generates the key on the voiceprint database with the generation intensity of more than 1000bit, the accuracy rate of more than 93%, and the error recognition rate of less than 0.001%. These data can prove that the voiceprint key generation model proposed in this paper can meet the needs of users for generation strength and security.

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Acknowledgements

This research is supported by Key Projects of NSFC Joint Fund of China (No. U1866209), National Natural Science Foundation of China (No. 61772162), National Key R &D Program of China (No. 2018YFB0804102).

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Authors and Affiliations

  1. Hangzhou Dianzi University, Hangzhou, China

    Zhengyin Lv, Zhendong Wu & Juan Chen

Authors
  1. Zhengyin Lv
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  2. Zhendong Wu
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  3. Juan Chen
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Corresponding author

Correspondence to Zhendong Wu .

Editor information

Editors and Affiliations

  1. School of Computing and Informatics, University of Louisiana at Lafayette, Lafayette, IN, USA

    Yuan Xu

  2. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Hongyang Yan

  3. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Huang Teng

  4. Guangdong Polytechnic Normal University, Guangzhou, China

    Jun Cai

  5. Institute of Artificial Intelligence and Blockchain, Guangzhou University, Guangzhou, China

    Jin Li

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Lv, Z., Wu, Z., Chen, J. (2023). Short Speech Key Generation Technology Based on Deep Learning. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_36

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  • DOI: https://doi.org/10.1007/978-3-031-20099-1_36

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20098-4

  • Online ISBN: 978-3-031-20099-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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