Evolutionary fusion of classifiers trained on linear prediction based features for replay attack detection
Babak Nasersharif, Morteza Yazdani · Expert Systems · 2021
Abstract Recently, linear prediction analysis (LP) related features have been successfully used for replay attack detection due to the imperfection in the LP‐based signal produced by recording and playback devices. In this paper, we propose a weighted linear combination of classifier scores for replay attack detection where our classifiers, including Gaussian mixture models (GMMs) and support vector machines (SVMs), are trained on a variety of LP and LP residual‐based features. In this way, we can benefits from all of the LP‐related features when we combine classifiers trained on these features. We determine classifier weights using two evolutionary algorithms: genetic algorithm and particle swarm optimization. Furthermore, we propose a new feature based on performing LP residuals analysis of Mel sub‐band energies. We also propose a deep structure for extracting deep features from LP‐based coefficients to consider the class labels (genuine or spoofed speaker) in the feature extraction process. Results of our classifier system on the ASVspoof 2017 version 2 dataset show equal error rates of 0.3% and 4.8% for its development and evaluation subset, respectively. We also applied our proposed replay attack detection method to an ASV system that has acceptable results.