用神经组装模型处理语音，无需训练就能识别音素和词边界。
Beyond Deep Learning: Speech Segmentation and Phone Classification with Neural Assemblies
- 通过概率梅尔二值化与群体编码的MFCC，将连续语音转为脉冲模式。
- 在无权重训练下实现音素边界的F1=0.69，词边界F1=0.61。
- 适合对生物可解释性、低数据需求语音处理感兴趣的读者。
深度学习主导语音处理，但依赖海量数据、全局反向传播更新权重，并产生纠缠表示。神经组装演算（AC）基于赫布可塑性和胜者为王竞争，模拟稀疏神经元集合，具有生物学基础，但以往研究仅针对离散符号输入。本文提出一种直接作用于连续语音的AC语音处理框架，包含三项核心贡献：(i) 神经编码机制，利用概率梅尔二值化与群体编码的MFCC，将语音转换为适配组装的脉冲模式；(ii) 多区域架构，在层级时间尺度与类别上组织神经组装；(iii) 跨区域更新策略用于下游任务。应用于边界检测与段落分类两项核心任务，该框架在无任何权重训练条件下，实现了音素边界检测的F1=0.69、词边界检测的F1=0.61，音素识别准确率47.5%，命令识别准确率45.1%。结果表明，基于AC的动力系统是深度学习在语音处理中的可行替代方案。
原文摘要 · Abstract (English)
Deep learning dominates speech processing but relies on massive datasets, global backpropagation-guided weight updates, and produces entangled representations. Assembly Calculus (AC), which models sparse neuronal assemblies via Hebbian plasticity and winner-take-all competition, offers a biologically grounded alternative, yet prior work focused on discrete symbolic inputs. We introduce an AC-based speech processing framework that operates directly on continuous speech by combining three key contributions:(i) neural encoding that converts speech into assembly-compatible spike patterns using probabilistic mel binarisation and population-coded MFCCs; (ii) a multi-area architecture organising assemblies across hierarchical timescales and classes; and (iii) cross-area update schemes for downstream tasks. Applied to two core tasks of boundary detection and segment classification, our framework detects phone (F1=0.69) and word (F1=0.61) boundaries without any weight training, and achieves 47.5% and 45.1% accuracy on phone and command recognition. These results show that AC-based dynamical systems are a viable alternative to deep learning for speech processing.
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