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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2026
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2605.13099 |
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| _version_ | 1866911679597510656 |
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| author | Xiao, Boda Wang, Bo Cheng, Heping |
| author_facet | Xiao, Boda Wang, Bo Cheng, Heping |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_13099 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval Xiao, Boda Wang, Bo Cheng, Heping Sound 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. |
| title | Bypassing Direct Reconstruction: Speech Detection from MEG via Large-Scale Audio Retrieval |
| topic | Sound |
| url | https://arxiv.org/abs/2605.13099 |