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Multimedia Tools and Applications

, Volume 78, Issue 2, pp 1569–1582 | Cite as

Person authentication using speech as a biometric against play back attacks

  • A. Revathi
  • C. JeyalakshmiEmail author
  • K. Thenmozhi
Article
  • 44 Downloads

Abstract

This work presents the modules for authenticating the persons by using speech as a biometric against recorded playback attacks. It involves the implementation of feature extraction, modeling technique and testing procedure for authenticating the persons. Playback attacks are simulated by recording the original speech utterances using the speakers and mikes in a laptop using Audacity software. This work mainly involves the process for distinguishing original and recorded speeches and authenticating the speakers based on voice as a biometric. Features extracted from the original and recorded speeches are used to develop models for them. Voice passwords are assigned to the speakers and features are extracted from the training speech created by fusing the password specific original speech utterances. These features are applied to the training algorithm to generate password specific speaker models. Testing procedure involves the feature extraction and application of features to the models pertaining to recorded and original speech models. If the test speech belongs to the recorded speech, it is prevented from undergoing the further process. If it is an original speech, feature vectors of the test speech are applied to the password specific speaker models and based on the classification criteria, a speaker is identified and authenticated. Our system is found to be robust against playback attacks and has given better performance in authenticating sixteen speakers considered in our work. Passwords are isolated words and digits chosen from “TIMIT” speech database. This work is also extended to using AVSpoof database for authenticating 44 speakers against replay attacks and the performance is analyzed in terms of rejection rate.

Keywords

Mel frequency perceptual linear predictive cepstrum (MFPLPC) Probabilty Playback attacks Robustness Speaker authentication Vector quantization (VQ) Replay attacks Peak signal to noise ratio (PSNR) Rejection rate 

Notes

Compliance with ethical standards

Competing Interest

The authors have declared that no competing interest exists.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of ECE/SEEESASTRA Deemed UniversityThanjavurIndia
  2. 2.Department of ECEK.Ramakrishnan College of EngineeringTrichyIndia

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