Development of Countermeasures for Voice Liveness and Spoofed Speech Detection

Abstract

An Automatic Speaker Verification (ASV) or voice biometric system performs machine based authentication of speakers using voice signals. ASV is a voice biometric system which has applications, such as banking transactions using mobile phones. Personal information, and banking details, demand more robust security of ASV systems. Furthermore, the Voice Assistants (VAs) are also known for the convenience of controlling most of the surrounding devices, such as user�s personal device, door locks, electric appliances, etc. However, these ASV and VA systems are also vulnerable to various spoofing attacks, such as details, twins, Voice Conversion (VC), Speech Synthesis (SS), and replay. In particular, the user�s voice command can be conveniently recorded and played back by the imposter (attacker) with negligible cost. Hence, the most harmful attack (replay attack) of morphing user�s voice command can be performed easily. Hence, this thesis aims to develop countermeasure to protect these ASV and VA systems from replay attacks. In addition, this thesis is also an attempt to develop Voice Liveness Detection (VLD) task as countermeasure for replay attack. In this thesis, the novel Cochlear Filter Cepstral Coefficients based Instanta neous Frequency using Quadrature Energy Separation Algorithm (CFCCIF-QESA) feature set is proposed for replay Spoofed Speech Detection (SSD) on ASV systems. Performance of the proposed feature set is evaluated using publicly avail- able datasets such as, ASVSpoof 2017 v2.0 and BTAS 2016. Furthermore, the significance of Delay and Sum (DAS) beamformer over state of the art Minimum Variance Distortionless Response (MVDR) for replay SSD on VAs. Finally, the wavelet based features are proposed for VLD task. The performance of proposed wavelet-based approaches are evaluated using recently released POp noise COr pus (POCO).