The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
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Computer Science > Computation and Language
arXiv:2609.10296 (cs)
[Submitted on 9 Sep 2026]
Title:The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding
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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.
| Comments: | 12 pages, 8 figures |
| Subjects: | Computation and Language (cs.CL); Machine Learning (cs.LG) |
| Cite as: | arXiv:2609.10296 [cs.CL] |
| (or arXiv:2609.10296v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.10296
arXiv-issued DOI via DataCite (pending registration)
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View a PDF of the paper titled The Semantic Bottleneck: Leveraging Semantic Representations for Non-Invasive Speech Decoding, by Gilad D. Landau and 2 other authors
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