Anti-Spoofing System: An Investigation of Measures to Detect Synthetic and Human Speech

Automatic Speaker Verification (ASV) systems are prone to spoofing attacks of various kinds. In this study, we explore the effects of different features and spoofing algorithms on a state-of-the-art i-vector speaker verification system. Our study is based on the standard dataset and evaluation protocols released as part of the ASVspoof 2015 challenge. We compare how different features perform while detecting both genuine and spoofed speech. We observe that features that contain phase information (Modified Group Delay based features) are better in detecting synthetic speech, and give comparable performance when compared to standard MFCCs. We report an anti-spoofing system that performs well both on known as well as unknown spoofing attacks.