XGB-RFE: An XGBoost Approach for Improved Playback Spoofing Detection in Automatic Speaker Verification Systems Using Recursive Feature Elimination
Harshil Sanghvi, Sapan H. Mankad · 2023
Playback spoofing, the act of impersonating another speaker by playing back recordings of their voice, poses a significant threat tot he security o f Automatic Speaker Veri-fication (ASV) systems. This paper introduces anew method for detecting playback spoofing in A SV systems. O ur approach combines temporal and spectral features with Machine Learning (ML) and Deep Learning (DL) techniques to distinguish between playback spoofed and genuine speech. We employed Recursive Feature Elimination (RFE) to retain the most relevant features, improving performance. The results of our evaluation on a subset of ASV spoof 2019 dataset show that the combination of RFE and XGBoost produced outstanding results, with accuracy rising from 89.07% to 99.86% and Equal Error Rate (EER) decreasing from 48.05% to a mere 0.69%. Our study highlights the significance of RFE in enhancing the performance of speaker verification systems and the potential of XGBoost as an algorithm for detecting playback spoofing attacks.