MEGConformer：基于Conformer的脑磁图解码器实现鲁棒性语音及音素分类

摘要

我们为2025年LibriBrain PNPL竞赛提出基于Conformer架构的解码器，针对两项基础性MEG任务：语音检测与音素分类。该方法将紧凑型Conformer适配于306通道原始MEG信号，采用轻量级卷积投影层和任务专用头部。在语音检测任务中，面向MEG的SpecAugment技术首次探索了MEG特异性数据增强策略。音素分类任务则采用逆平方根类别加权和动态分组加载器来处理100样本平均化的示例。此外，简单的实例级归一化对缓解留出集上的分布偏移起到关键作用。使用官方标准赛道划分和F1-macro指标进行模型选择后，我们的最佳系统在排行榜上分别达到88.9%（语音检测）和65.8%（音素分类）的准确率，超越竞赛基线并在两项任务中均位列前十。具体实现细节、技术文档、源代码及模型检查点详见https://github.com/neural2speech/libribrain-experiments。

English

We present Conformer-based decoders for the LibriBrain 2025 PNPL competition, targeting two foundational MEG tasks: Speech Detection and Phoneme Classification. Our approach adapts a compact Conformer to raw 306-channel MEG signals, with a lightweight convolutional projection layer and task-specific heads. For Speech Detection, a MEG-oriented SpecAugment provided a first exploration of MEG-specific augmentation. For Phoneme Classification, we used inverse-square-root class weighting and a dynamic grouping loader to handle 100-sample averaged examples. In addition, a simple instance-level normalization proved critical to mitigate distribution shifts on the holdout split. Using the official Standard track splits and F1-macro for model selection, our best systems achieved 88.9% (Speech) and 65.8% (Phoneme) on the leaderboard, surpassing the competition baselines and ranking within the top-10 in both tasks. For further implementation details, the technical documentation, source code, and checkpoints are available at https://github.com/neural2speech/libribrain-experiments.

MEGConformer：基于Conformer的脑磁图解码器实现鲁棒性语音及音素分类

MEGConformer: Conformer-Based MEG Decoder for Robust Speech and Phoneme Classification

摘要

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