| title | The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding |
|---|---|
| authors | Gilad D. Landau, Dulhan Jayalath, Oiwi Parker Jones |
| date | 2026-09-09 |
| venue | arXiv (preprint, not peer reviewed) |
| id | arXiv:2609.10296 |
| affiliation | Parker Jones Neural Processing Lab (PNPL) |
| upvotes | 3 (Hugging Face Daily Papers, 2026-11-01) |
| links | abs pdf hf |
| abstract | 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 predicted embeddings into natural language. This semantic bottleneck enables recovery of high-level meaning without word-level alignment. We describe the core principles of the approach, its implementation, and the strategies used to mitigate the challenges of learning a reliable neural-to-semantic mapping. Finally, we compare against prior non-invasive Brain2Text methods and show improved sentence-level results. |
cite
@misc{landau2026semantic,
title={The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding},
author={Gilad D. Landau and Dulhan Jayalath and Oiwi Parker Jones},
year={2026},
eprint={2609.10296},
archivePrefix={arXiv},
url={https://arxiv.org/abs/2609.10296}
}