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Investigating Feature Reduction Strategies for Replay Antispoofing in Voice Biometrics

  • Sapan H. MankadEmail author
  • Sanjay Garg
  • Megh Patel
  • Harshil Adalja
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11942)

Abstract

One of the biggest challenges for voice based biometric solutions is in handling replay spoofing attacks. These attacks pose enormous threat on speaker verification system wherein the recorded voice of a genuine user is played in front of the authentication system to attempt unauthorized access. The problem with such system is to distinguish between origin of the input signal whether it comes from a human (live signal) or a device (spoofed signal). In this work, we compare filterbank based features and attempt to choose prominent features by employing some dimensionality reduction strategies. Low level, short-term spectral features have been used to represent audio files. Three methods for feature selection and feature construction are implemented and tested on these features. Results obtained on ASVspoof 2017 version 2 corpus indicate that entropy based feature selection approach gains 9.98% relative improvement over other approaches for feature reduction studied in this work, and an overall performance gain of 13.2% in terms of equal error rate reduction.

Keywords

Replay attacks ASVspoof 2017 Feature selection Entropy Dimensionality reduction 

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Sapan H. Mankad
    • 1
    Email author
  • Sanjay Garg
    • 1
  • Megh Patel
    • 1
  • Harshil Adalja
    • 1
  1. 1.CSE Department, Institute of TechnologyNirma UniversityAhmedabadIndia

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