# Ensemble Models for Spoofing Detection in Automatic Speaker Verification

## TL;DR

This paper develops ensemble models combining deep neural networks and traditional machine learning to improve spoofing detection in automatic speaker verification, demonstrating superior performance over single models and analyzing dataset characteristics affecting results.

## Contribution

It introduces an ensemble approach for spoofing detection that enhances robustness and proposes dataset partitioning strategies to improve model generalization.

## Key findings

- Ensemble models outperform individual models and challenge baselines.
- Removing long silences in spoofed recordings increases detection difficulty.
- Dataset partitioning improves robustness across attack types.

## Abstract

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. Our ensemble model outperforms all our single models and the baselines from the challenge for both attack types. We investigate why some models on the PA dataset strongly outperform others and find that spoofed recordings in the dataset tend to have longer silences at the end than genuine ones. By removing them, the PA task becomes much more challenging, with the tandem detection cost function (t-DCF) of our best single model rising from 0.1672 to 0.5018 and equal error rate (EER) increasing from 5.98% to 19.8% on the development set.