Do Speech BCIs Need Larger Models? Rethinking Neural Decoding beyond Scaling

Title:Do Speech BCIs Need Larger Models? Rethinking Neural Decoding beyond Scaling

Abstract:Recent speech brain-computer interfaces (BCIs) increasingly rely on large models and complex multi-stage pipelines for neural decoding. However, in speech BCI, where training data are scarce, noisy, and expensive to collect, the benefits of model scaling remain unclear. This work revisits a fundamental question: are large models truly necessary for accurate neural decoding? We propose BEST (Brain-Encoding-Speech-Text), a two-stage framework for efficient neural-to-text decoding. Instead of relying on large generative models or phoneme-centric pipelines, BEST introduces hierarchical intermediate representations and a compact speech-oriented decoder that directly maps neural activity to text. This design preserves essential structure while significantly reducing computational cost, enabling robust generalization in low-resource settings and practical deployment under constrained resources. On the Brain-to-Text '24 and '25 benchmarks, BEST achieves word error rates (WERs) of 6.19% and 1.62%, respectively, using only 3.85% of the parameters required by comparable methods. Beyond these results, we provide three key insights: (1) decoder scaling yields non-monotonic gains; (2) cross-modal alignment is more critical than model size; and (3) overfitting degards sequence-level performance. Representation analysis further reveals a consistent neural-audio-text alignment pathway.

Computation and Language (cs.CL)

arXiv:2610.23175 [cs.CL]

(or arXiv:2610.23175v1 [cs.CL] for this version)

https://doi.org/10.48550/arXiv.2610.23175 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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