Unit-Selection Attack Detection Based on Unfiltered Frequency-Domain Features

Modern text-to-speech algorithms pose a vital threat to the security of speaker identification and verification (SIV) systems, in terms of subversive usage, i.e. generating presentation attacks. In order to distinguish between presentation attacks and bona fide authentication attempts, presentation attack detection (PAD) subsystems are of utmost importance. Until now, the vast majority of introduced spoofing countermeasures rely on speech production and perception based features. In this paper, we utilize the complete frequency band without further filterbank processing in order to detect non-smooth transitions in the full and high frequency domain caused by unit-selection attacks. For the purpose of especially detecting unit selection attacks, the applicability of Fast Fourier Transformation (FFT) and Discrete Wavelet Transformation (DWT) is examined regarding non-smooth transitions in the full and high frequency domain, excluding filter-bank analyses. Gaussian Mixture Model (GMM) and Support Vector Machine (SVM) classifiers are trained on the German Speech Data Corpus (GSDC) and validated on the standard ASVspoof 2015 corpus resulting in EERs of 7.1% and 11.7%, respectively. Despite language and data shifts, the proposed unit-selection PAD scheme achieves promising biometric performance and hence, introduces a new direction to voice PAD.