Ensemble Models for Spoofing Detection in Automatic Speaker Verification

Understand

Detecting spoofing attempts of automatic speaker verification (ASV) systems is challenging, especially when using only one modeling approach.

• For robustness, we use both deep neural networks and traditional machine learning models and combine them as ensemble models through logistic regression.
• They are trained to detect logical access (LA) and physical access (PA) attacks on the dataset released as part of the ASV Spoofing and Countermeasures Challenge 2019.
• We propose dataset partitions that ensure different attack types are present during training and validation to improve system robustness.