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Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval

2026-05-13 07:11:13
Boda Xiao, Bo Wang, Heping Cheng
arXiv_SD
             
Abstract

Decoding speech from non-invasive brain signals is challenging. For the LibriBrain 2025 Speech Detection task, we propose a novel two-step framework that bypasses direct reconstruction. First, a contrastive learning model retrieves the matching speech segment for the given test MEG from a large-scale audio library (LibriVox). Second, a speech detection model generates the binary silence/speech sequence directly from this retrieved audio. With this approach, our team Sherlock Holmes achieved first place in the extended track (F1-score: 0.962), demonstrating that leveraging external audio databases is a highly effective strategy.

Abstract (translated)

URL

https://arxiv.org/abs/2605.13099

PDF

https://arxiv.org/pdf/2605.13099.pdf

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