Automatic Spoofing Detection Using Deep Learning

Abstract

Deep fakes stand out to be the most dangerous side effects of Artificial Intelligence. AI assists to produce voice cloning of any entity which is very arduous to categorize whether it’s fake or real. The aim of the research is to impart a spoofing detection system to an automatic speaker verification (ASV) system that can perceive false voices efficiently. The goal is to perceive the unapparent audio elements with maximum precision and to develop a model that is proficient in automatically extracting audio features by utilizing the ASVspoof 2019 dataset. Hence, the proposed ML-DL SafetyNet model is designed that delicately differentiate ASVspoof 2019 dataset voice speeches into fake or bonafide. ASVspoof 2019 dataset is characterized into two segments LA and PA. The ML-DL SafetyNet model is centred on two unique processes; deep learning and machine learning classifiers. Both techniques executed strong performance by achieving an accuracy of 90%.