MindVoice: Towards Intelligible Speech Reconstruction from Non-invasive Neural Signals via Linguistic Recovery

Reconstructing speech from neural activity offers a direct window into human auditory perception and holds promise for safe and scalable speech brain-computer interfaces. Yet intelligible reconstruction from non-invasive neural signals remains challenging. Existing methods are predominantly acoustic-centric, directly mapping noisy neural recordings to entangled speech representations, which can preserve acoustic characteristics while failing to reliably recover the underlying linguistic content. We introduce *MindVoice*, a neural speech reconstruction framework that addresses this *intelligibility gap* through explicit linguistic recovery. A linguistic reconstruction stream estimates what was perceived, while a complementary acoustic stream captures fine-grained characteristics of how it was spoken. Leveraging pretrained speech priors to compensate for incomplete neural evidence, *MindVoice* integrates both streams through text-to-speech generation and in-context voice cloning to synthesize natural, intelligible speech. Experiments on EEG and MEG datasets demonstrate substantial intelligibility gains over existing non-invasive methods. Further analyses reveal an empirical trade-off between spectral fidelity and linguistic intelligibility, with *MindVoice* achieving a favorable balance. Together, these findings support explicit linguistic recovery as an effective route toward intelligible non-invasive neural speech reconstruction.