A 12.9M-parameter audio-to-fMRI encoder achieves zero-shot prediction of speech-evoked brain responses on 324 unseen participants and few-shot adaptation with ~10 minutes of data, outperforming larger baselines and per-participant ridge regression.
WavLM: Large-scale self-supervised pre- training for full stack speech processing.IEEE Journal of Selected Topics in Signal Processing, 16(6):1505–1518, 2022
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RABBiT: Rapidly adaptive BOLD foundation model via brain-tuning for accurate zero-shot and few-shot prediction of speech-elicited responses in the brain
A 12.9M-parameter audio-to-fMRI encoder achieves zero-shot prediction of speech-evoked brain responses on 324 unseen participants and few-shot adaptation with ~10 minutes of data, outperforming larger baselines and per-participant ridge regression.