Breaking Shortcut Learning for Cross-Trial EEG-Guided Target Speech Extraction via Two-Stage Training
arXiv:2606.24164v1 Announce Type: cross Abstract: Recent end-to-end models for EEG-guided target speech extraction report impressive results, underscoring potential for neuro-steered hearing technologies. However, our analysis reveals that high within-trial performance can be driven by trial-specific EEG structure that acts as shortcuts for target selection, leading to poor generalization on unseen trials. To overcome this gap, we propose TRUST-TSE, a two-stage framework to mitigate shortcut learning. By introducing contrastive pretraining with attended-speaker negative sampling, we encourage
This research addresses a critical limitation in current EEG-guided speech extraction models, which is essential for advancing neuro-steered hearing technologies towards real-world applications.
Improving generalization in EEG-guided target speech extraction is crucial for reliable neuro-prosthetics and human-computer interfaces, impacting assistive technologies and cognitive monitoring.
The proposed two-stage training framework directly tackles 'shortcut learning,' enabling more robust and generalizable EEG-based signal processing for complex tasks.
- · Neuro-prosthetics industry
- · Hearing aid manufacturers
- · Patients with hearing impairment
- · AI researchers in brain-computer interfaces
- · Companies relying on less robust EEG signal processing methods
More accurate and reliable EEG-guided hearing assistance devices become feasible.
Improved neuro-steered devices could lead to enhanced cognitive performance and communication for individuals in diverse environments.
The principles of mitigating shortcut learning could be applied to other bio-signal processing challenges, accelerating the development of advanced medical AI.
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Read at arXiv cs.AI