Acoustic features exploration and examination for voice spoofing counter measures with boosting machine learning techniques

Automatic Speaker verification systems are vulnerable to spoofing attacks. We propose our anti-spoofing system. It uses some acoustic features. The set of binary classifiers includes XGBoost tree boosting algorithm. ASV Spoof 2015 corpus is utilized in the experiments as the main database for anti-spoofing systems training. The pretreatment of acoustic features is essential for better performance of the system. Obtained results demonstrate that the proposed system can provide a good accuracy. High evaluation performance can be obtained using the combination between MFCC, LogFBE and SSC features. The attained metrics values such 97.80% for the accuracy, 97.12% for the precision, 98.50% for the Recall and 97.81% for the F1-mesure validate the performance of the proposed technique.