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Playback Speech Detection Application Based on Cepstrum Feature

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Data Science (ICDS 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1179))

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Abstract

With the popularity of various portable recording devices, playback speech has become one of the most important means of attack in the speaker authentication system. By comparing with the original speech data, the difference in the high-frequency layer, and the playback speech is also different in the low-frequency layer due to the different recording equipment. According to this finding, a detection algorithm was presented to extract representative data. In the high frequency layer, the inverse-Mel filters (I-Mel) is used to extract speaker eigenvector sequences. In the low frequency layer, linear filters (Linear) is combined with Mel filters (Mel) to avoid superposition of characteristic parameters. Multi-layer fusion to obtain L-M-I filter banks to form new cepstral features. The experimental results show that the method can detect playback speech effectively and the equal error rate is 2.63%. Compared with the traditional feature extraction methods (MFCC, CQCC, LFCC, IMFCC), the equal error rate decreases by 12.79%, 9.61%, 4.45% and 3.28% respectively.

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Acknowledgements

This work was funded by the Natural Science Foundation of Jiangsu Province (Project No. BK20150987) and the support of the College of Information Engineering, Nanjing University of Finance & Economics. In addition, authors would like to thank the database provided by the ASVspoof2017 challenge.

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Authors and Affiliations

  1. College of Information Engineering, Nanjing University of Finance and Economics, Nanjing, 210023, Jiangsu, China

    Jing Zhou & Ye Jiang

Authors
  1. Jing Zhou
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  2. Ye Jiang
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Corresponding author

Correspondence to Ye Jiang .

Editor information

Editors and Affiliations

  1. Swinburne University of Technology, Melbourne, VIC, Australia

    Jing He

  2. University of Illinois at Chicago, Chicago, USA

    Philip S. Yu

  3. College of Information Science and Technology, University of Nebraska at Omaha, Omaha, NE, USA

    Yong Shi

  4. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou, China

    Xingsen Li

  5. Ningbo University, Ningbo, China

    Zhijun Xie

  6. Deakin University, Burwood, VIC, Australia

    Guangyan Huang

  7. Department of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing, China

    Jie Cao

  8. Nanjing University of Posts and Telecommunications, Nanjing, China

    Fu Xiao

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Zhou, J., Jiang, Y. (2020). Playback Speech Detection Application Based on Cepstrum Feature. In: He, J., et al. Data Science. ICDS 2019. Communications in Computer and Information Science, vol 1179. Springer, Singapore. https://doi.org/10.1007/978-981-15-2810-1_24

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  • DOI: https://doi.org/10.1007/978-981-15-2810-1_24

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-2809-5

  • Online ISBN: 978-981-15-2810-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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