MindVoice disentangles neural-to-speech reconstruction into semantic and acoustic pathways using pretrained priors, then fuses them with speech generation models to produce intelligible output from non-invasive recordings.
Libribrain: Over 50 hours of within-subject meg to improve speech decoding methods at scale
3 Pith papers cite this work. Polarity classification is still indexing.
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Imagined speech can be decoded from MEG by mapping imagined brain responses to listened ones and applying a word decoder trained only on listened data, yielding significant above-chance decoding for held-out subjects.
Retrieval from an external audio library via contrastive MEG-to-audio matching yields top-ranked speech detection performance without direct brain-to-waveform reconstruction.
citing papers explorer
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MindVoice: Reconstructing Intelligible Speech from Non-invasive Neural Signals with Pretrained Priors
MindVoice disentangles neural-to-speech reconstruction into semantic and acoustic pathways using pretrained priors, then fuses them with speech generation models to produce intelligible output from non-invasive recordings.
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Zero-Shot Imagined Speech Decoding via Imagined-to-Listened MEG Mapping
Imagined speech can be decoded from MEG by mapping imagined brain responses to listened ones and applying a word decoder trained only on listened data, yielding significant above-chance decoding for held-out subjects.
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Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval
Retrieval from an external audio library via contrastive MEG-to-audio matching yields top-ranked speech detection performance without direct brain-to-waveform reconstruction.