# Robust End-to-End Speaker Verification Using EEG

## TL;DR

This paper shows that combining EEG signals with speech features enhances the robustness of speaker verification systems, especially in noisy environments, using a state-of-the-art deep learning approach.

## Contribution

It introduces a novel end-to-end deep learning method that incorporates EEG signals to improve speaker verification performance in noisy conditions.

## Key findings

- EEG signals improve speaker verification accuracy in noisy environments
- Concatenating EEG with speech features enhances robustness
- EEG-only features can be effective for speaker verification

## Abstract

In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal features or only using EEG signal features. We use state-of-the-art end-to-end deep learning model for performing speaker verification and we demonstrate our results for noisy speech. Our results indicate that EEG signals can improve the robustness of speaker verification systems, especially in noiser environment.