A compact MEG-to-speech retrieval model with spherical-harmonic attention and per-branch temporal filters matches black-box accuracy while revealing cortical sources and the acoustic, phonetic, and surprisal features its decisions rely on.
wav2vec 2.0: A framework for self- supervised learning of speech representations
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Interpretable MEG Decoding of Perceived Speech: Cortical Sources and the Stimulus Features That Drive Retrieval
A compact MEG-to-speech retrieval model with spherical-harmonic attention and per-branch temporal filters matches black-box accuracy while revealing cortical sources and the acoustic, phonetic, and surprisal features its decisions rely on.