The GMM and I-Vector Systems Based on Spoofing Algorithms for Speaker Spoofing Detection

Abstract

Automatic Speaker Verification (ASV) systems are more vulnerable to being attacked than other biometric systems, such as speech synthesis, voice conversion, and replay. In this paper, two frameworks (Gaussian mixture model based and i-vector based) are used to detect a variety of specific attack types. Three scoring methods (probabilistic normalization, linear regression and support vector machine) are used for the Gaussian Mixture Model (GMM) model. And three different classifiers (cosine distance, probabilistic linear discriminant analysis, support vector machine) are used for the i-vector system. Furthermore, the cosine classifier based on the i-vector system which uses three scoring methods is proposed in this paper. Experiments on the ASVspoof 2019 challenge logical access scenario show that the GMM classifier with the Support Vector Machines (SVM) scoring method based on different spoofing algorithms obtains the best performance on the evaluation set with EER of 7.03%. Moreover, SVM scoring is also useful for improving the i-vector system based on different spoofing algorithms.