Speaker Identification using EEG

TL;DR

This paper demonstrates that EEG signals can significantly improve speaker identification accuracy, especially in noisy environments, outperforming acoustic features alone in high noise conditions.

Contribution

It introduces a novel approach of using EEG features for speaker identification, showing enhanced performance in noisy settings compared to traditional acoustic methods.

Findings

EEG features improve speaker identification accuracy.

EEG-based systems outperform acoustic-only systems in high noise.

EEG features are effective in both noisy and quiet environments.

Abstract

In this paper we explore speaker identification using electroencephalography (EEG) signals. The performance of speaker identification systems degrades in presence of background noise, this paper demonstrates that EEG features can be used to enhance the performance of speaker identification systems operating in presence and absence of background noise. The paper further demonstrates that in presence of high background noise, speaker identification system using only EEG features as input demonstrates better performance than the system using only acoustic features as input.