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Deep Learning Approach: Detection of Replay Attack in ASV Systems

  • S. SaranyaEmail author
  • Suvidha Rupesh Kumar
  • B. Bharathi
Conference paper
  • 17 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1118)

Abstract

Automatic speaker verification (ASV) system is a bio-metric authentication system, which accepts or rejects speech utterance depending on the voiceprint of speakers. Vulnerability of such system to spoofing attacks is the biggest challenge at hand. Proposed system is an initiative toward improvising the countermeasure for replay attacks on ASV systems. It is developed and tested on ASVspoof2017 corpus. Constant Q cepstral coefficients (CQCCs) feature is extracted to build the models using simple neural network and convolutional neural network. CQCC feature is chosen, as it a feature proved to be robust for ASV spoof detection when tested on GMM classifier. A significant improvement of 4.9% EER is achieved using convolution neural network (CNN).

Keywords

Speaker verification CQCC Spoofing Replay attack Neural network CNN 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of CSESSN College of EngineeringChennaiIndia

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