[2604.04129] Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shift

Computer Science > Sound arXiv:2604.04129 (cs) [Submitted on 5 Apr 2026] Title:Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shift Authors:Sheng-You Chien, Bo-Yi Mao, Yi-Ning Chang, Po-Chih Kuo View a PDF of the paper titled Measuring Robustness of Speech Recognition from MEG Signals Under Distribution Shift, by Sheng-You Chien and 3 other authors View PDF HTML (experimental) Abstract:This study investigates robust speech-related decoding from non-invasive MEG signals using the LibriBrain phoneme-classification benchmark from the 2025 PNPL competition. We compare residual convolutional neural networks (CNNs), an STFT-based CNN, and a CNN--Transformer hybrid, while also examining the effects of group averaging, label balancing, repeated grouping, normalization strategies, and data augmentation. Across our in-house implementations, preprocessing and data-configuration choices matter more than additional architectural complexity, among which instance normalization emerges as the most influential modification for generalization. The strongest of our own models, a CNN with group averaging, label balancing, repeated grouping, and instance normalization, achieves 60.95% F1-macro on the test split, compared with 39.53% for the plain CNN baseline. However, most of our models, without instance normalization, show substantial validation-to-test degradation, indicating that distribution shift induced by different normalization statistics is a major obs...