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Replay Attack Detection for Speaker Verification Using Different Features Level Fusion System

In the present scenario, the popularity of Automatic speaker verification (ASV) is on an increasing note. The authentication of individuals by doing analysis on speech utterances represents an ASV. However, the speech signals are spoofed under real time scenario and it becomes much more difficult to differentiate between genuine and spoof speech samples. Hence, it becomes vulnerable to replay attacks which need to be deal with. Recently, it has been widely used in many smart devices that require user verification, such as smart speakers and smartphones. In order to overcome this problem, we propose new approach using fusion of different feature extraction techniques together to improve the system performance against the replay attack for speaker verification system by reducing equal error rate (EER). In this work, we fused constant-Q cepstral coefficients (CQCC), linear prediction cepstral coefficients (LPCC) and linear frequency cepstral coefficients (LFCC) methods and yields better results compare to the other tradition stand-alone feature extraction methods. ASV spoof-2017 database is used to evaluate the proposed system, and simulation results were conducted using MATLAB 2019.