Anti-spoofing Methods for Automatic SpeakerVerification System

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Authors: Galina Lavrentyeva, Sergey Novoselov, Konstantin Simonchik

Published: 2017-05-24 16:58:03+00:00

AI Summary

This research paper analyzes various acoustic feature spaces and classifiers for robust spoofing detection in automatic speaker verification systems. It compares different spoofing detection systems on the ASVspoof 2015 challenge datasets, finding that combining magnitude and phase information, along with wavelet-based features, yields improved performance.

Abstract

Growing interest in automatic speaker verification (ASV)systems has lead to significant quality improvement of spoofing attackson them. Many research works confirm that despite the low equal er-ror rate (EER) ASV systems are still vulnerable to spoofing attacks. Inthis work we overview different acoustic feature spaces and classifiersto determine reliable and robust countermeasures against spoofing at-tacks. We compared several spoofing detection systems, presented so far,on the development and evaluation datasets of the Automatic SpeakerVerification Spoofing and Countermeasures (ASVspoof) Challenge 2015.Experimental results presented in this paper demonstrate that the useof magnitude and phase information combination provides a substantialinput into the efficiency of the spoofing detection systems. Also wavelet-based features show impressive results in terms of equal error rate. Inour overview we compare spoofing performance for systems based on dif-ferent classifiers. Comparison results demonstrate that the linear SVMclassifier outperforms the conventional GMM approach. However, manyresearchers inspired by the great success of deep neural networks (DNN)approaches in the automatic speech recognition, applied DNN in thespoofing detection task and obtained quite low EER for known and un-known type of spoofing attacks.

Key findings

Combining magnitude and phase information significantly improves spoofing detection system efficiency. Wavelet-based features show impressive results in terms of equal error rate. Linear SVM classifiers generally outperformed GMM approaches, though DNNs also demonstrated low EERs.

Approach

The authors compared existing spoofing detection systems using the ASVspoof 2015 challenge datasets. They evaluated various acoustic features (e.g., MFCCs, phase-based features, wavelet-based features) and classifiers (e.g., SVM, GMM, DNN) to identify effective countermeasures against spoofing attacks.

Datasets

ASVspoof 2015 Challenge datasets (training, development, and evaluation sets)

Model(s)

GMM, SVM, DNN, Deep Belief Network (DBN)

Author countries

Russia

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