Spoofing Detection and Countermeasure in Automatic Speaker Verification System using Dynamic Features
Medikonda Neelima1, I. Santi Prabha2
1Medikonda Neelima*, Ph.D. Scholar, E.C.E. Department, JNTUK, Kakinada, Andhra Pradesh, India.
2I. Santi Prabha, Professor, E.C.E. Department, JNTUK, Kakinada, Andhra Pradesh, India. 

Manuscript received on January 05, 2020. | Revised Manuscript received on January 25, 2020. | Manuscript published on January 30, 2020. | PP: 3676-3680 | Volume-8 Issue-5, January 2020. | Retrieval Number: E6582018520/2020©BEIESP | DOI: 10.35940/ijrte.E6582.018520

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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: This present paper aims to extract robust dynamic features used to spoofing detection and countermeasure in ASV system. ASV is a biometric person authentication system. Researchers are aiming to develop spoofing detection and countermeasure techniques to protect this system against different spoofing attacks. For this, replayed attack is considered, because of very common accessibility of recording devices. In replay spoofing, the speech utterances of target (genuine) speakers are recorded and played against ASV system for gaining access unauthorizedly. For this purpose, as a first step, different dynamic features will be extracted for each speech sample. For feature extraction MFCC, LFCC, and MGDCC feature extraction techniques are used. As a second step, a classifier is used to classify whether the given speech sample is genuine or not. As a classifier, GMM and universal background model is used. In this present work, GMM based ASV system and Countermeasure systems using different feature extraction techniques are developed, and the performance of the methods is evaluated using EER and t- DCF. Basing on the performance values, the best feature extraction technique is selected.
Keywords: Automatic Speaker Verification (ASV) system, Equal Error Rate (EER), False Acceptance Rate (FAR), False Rejection Rate (FRR).
Scope of the Article: Foundations Dynamics.