Development of Voice Spoofing Detection Systems for 2019 Edition of Automatic Speaker Verification and Countermeasures Challenge

Abstract

A robust speaker verification system is expected to provide high recognition accuracy not only in adverse environments but also in the presence of spoofing attacks, which renders voice spoofing detection as crucial to prevent automatic speaker verification systems from a security breach. In this work, we present anti-spoofing systems developed for tackling spoofing attacks introduced for the ASVspoof 2019 challenge. We employ frame-level descriptors such as discrete Fourier transform, as well as constant Q transform-based spectral and cepstral features as countermeasures. These descriptors are both used on their own with a spoofing detection classifier to detect spoofing attacks, or in tandem with deep bottleneck features, i.e. approximate posteriors parametrized by a neural network designed to discriminate between bonafide and spoof signals. Fisher vector encoding and i-vector representations are further learned from the frame-level descriptors of the signals. For modeling, we employ two classification strategies. We finally build an end-to-end anti-spoofing system by making use of modified versions of light convolution neural networks as well as well-known ResNets. Our primary system for the logical access task and a single end-to-end system for the case of physical access we attain significant improvements over two baseline systems.