Replay detection in voice biometrics: an investigation of adaptive and non-adaptive front-ends

Among various physiological and behavioural traits, speech has gained popularity as an effective mode of biometric authentication. Even though they are gaining popularity, automatic speaker verification systems are vulnerable to malicious attacks, known as spoofing attacks. Among various types of spoofing attacks, replay attack poses the biggest threat due to its simplicity and effectiveness. This thesis investigates the importance of 1) improving front-end feature extraction via novel feature extraction techniques and 2) enhancing spectral components via adaptive front-end frameworks to improve replay attack detection. This thesis initially focuses on AM-FM modelling techniques and their use in replay attack detection. A novel method to extract the sub-band frequency modulation (FM) component using the spectral centroid of a signal is proposed, and its use as a potential acoustic feature is also discussed. Frequency Domain Linear Prediction (FDLP) is explored as a method to obtain the temporal envelope of a speech signal. The temporal envelope carries amplitude modulation (AM) information of speech resonances. Several features are extracted from the temporal envelope and the FDLP residual signal. These features are then evaluated for replay attack detection and shown to have significant capability in discriminating genuine and spoofed signals. Fusion of AM and FM-based features has shown that AM and FM carry complementary information that helps distinguish replayed signals from genuine ones. The importance of frequency band allocation when creating filter banks is studied as well to further advance the understanding of front-ends for replay attack detection. Mechanisms inspired by the human auditory system that makes the human ear an excellent spectrum analyser have been investigated and integrated into front-ends. Spatial differentiation, a mechanism that provides additional sharpening to auditory filters is one of them that is used in this work to improve the selectivity of the sub-band decomposition filters. Two features are extracted using the improved filter bank front-end: spectral envelope centroid magnitude (SECM) and spectral envelope centroid frequency (SECF). These are used to establish the positive effect of spatial differentiation on discriminating spoofed signals. Level-dependent filter tuning, which allows the ear to handle a large dynamic range, is integrated into the filter bank to further improve the front-end. This mechanism converts the filter bank into an adaptive one where the selectivity of the filters is varied based on the input signal energy. Experimental results show that this leads to improved spoofing detection performance. Finally, deep neural network (DNN) mechanisms are integrated into sub-band feature extraction to develop an adaptive front-end that adjusts its characteristics based on the sub-band signals. A DNN-based controller that takes sub-band FM components as input, is developed to adaptively control the selectivity and sensitivity of a parallel filter bank to enhance the artifacts that differentiate a replayed signal from a genuine signal. This work illustrates gradient-based optimization of a DNN-based controller using the feedback from a spoofing detection back-end classifier, thus training it to reduce spoofing detection error. The proposed framework has displayed a superior ability in identifying high-quality replayed signals compared to conventional non-adaptive frameworks. All techniques proposed in this thesis have been evaluated on well-established databases on replay attack detection and compared with state-of-the-art baseline systems