Robust End to End Speaker Verification Using EEG

06/17/2019

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In this paper we demonstrate that performance of a speaker verification system can be improved by concatenating electroencephalography (EEG) signal features with speech signal. We use state of 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.

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Robust End to End Speaker Verification Using EEG

PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction

EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses

Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions

Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing

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Robust End to End Speaker Verification Using EEG

Related Research

PL-EESR: Perceptual Loss Based END-TO-END Robust Speaker Representation Extraction

EEG-informed attended speaker extraction from recorded speech mixtures with application in neuro-steered hearing prostheses

Shouted Speech Compensation for Speaker Verification Robust to Vocal Effort Conditions

Detection of speech events and speaker characteristics through photo-plethysmographic signal neural processing

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