The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
Non-invasive speech decoding remains constrained by the low signal-to-noise ratio of neural recordings, which makes fine-grained reconstruction of phonemes or individual words difficult. Motivated by neuroscientific evidence that high-level semantic representations are distributed across cortical regions and evolve over slower temporal scales, we hypothesize that semantic content may provide a more suitable target for non-invasive decoding than low-level acoustic or lexical features. We introduce Brain2Semantics2Text, a method that reconstructs text through an intermediate semantic embedding space. Our model maps sentence-level MEG responses into a semantic manifold and then inverts the p...
| Authors | Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones |
|---|---|
| Primary category | cs.CL |
| Published | 9 September 2026 |
| Last updated at source | 7 October 2026 |
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