这篇论文给脉冲神经网络设计了个自适应语音编码器，参数少还能在GSC-v2上跑到94.97%，比很多大模型都强，还比较了两种训练方式。
提出可学习的残差语音到脉冲编码器，与Recurrent Leaky Integrate-and-Fire (R-LIF)骨干联合训练。在Google Speech Commands v2 (GSC-v2)基准上达到94.97%准确率。35k参数的紧凑变体达到89.8%，匹配或超越参数多一个数量级的基线。编码器学习任务对齐的脉冲表示，提升类别可分性。Direct Feedback Alignment (DFA)在相同设置下达到91.5%，量化了生物启发学习规则的性能权衡。
Adaptive Speech-to-Spike Encoding for Spiking Neural Networks
The mismatch between continuous acoustic signals and discrete event-driven processing remains a fundamental bottleneck for neuromorphic speech processing. Current systems typically rely on fixed spike encoders, forcing downstream Spiking Neural Networks (SNNs) to compensate for non-adaptive input representations. To address this, we present a learnable residual speech-to-spike encoder jointly trained end-to-end with a Recurrent Leaky Integrate-and-Fire (R-LIF) backbone. We validate this approach on the Google Speech Commands v2 (GSC-v2) benchmark, achieving up to 94.97% accuracy. Notably, the learned encoder remains highly parameter-efficient with a compact 35k-parameter variant that reaches 89.8%, matching or exceeding prior baselines that require an order of magnitude more parameters. Our encoder-focused analysis, including linear probing and gradient-residual inspection, indicates that the encoder does not target faithful signal reconstruction but instead learns task-aligned spike representations that enhance class separability. Finally, we benchmark bio-inspired, hardware-friendly credit assignment by comparing Direct Feedback Alignment (DFA) with surrogate-gradient BPTT under identical architectures and training conditions. We find that DFA reaches 91.5% accuracy, quantifying the performance trade-off of bio-inspired learning rules for modern neuromorphic audio.