Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training

The article introduces TRUST-TSE, a two-stage framework designed to improve generalization in EEG-guided target speech extraction by mitigating shortcut learning associated with trial-specific EEG structures. It employs contrastive pretraining with negative sampling and a confidence-weighted extraction objective to enhance EEG-speech alignment and suppress trial-identity cues. Experimental results on KUL and DTU datasets demonstrate that TRUST-TSE significantly outperforms existing end-to-end models under cross-trial evaluation, offering a more reliable solution for neuro-steered hearing technologies.