本文提出一种低资源语音命令识别器，结合能量型语音活动检测、优化MFCC流水线和LogNNet分类器。通过在8 kHz下采样Speech Commands数据集的四条命令进行评估，发现自适应分箱方法（64维特征向量）在准确性与紧凑性之间取得了最佳平衡。LogNNet分类器在独立于说话人的评估中达到92.04%的准确率，同时参数数量显著少于传统深度学习模型。在Arduino Nano 33 IoT硬件平台上验证了其实用性，实现了约90%的实时识别准确率，同时仅消耗18 KB RAM。

arXiv:2509.00862v1 Announce Type: cross Abstract: This paper presents a low-resource speech-command recognizer combining energy-based voice activity detection (VAD), an optimized Mel-Frequency Cepstral Coefficients (MFCC) pipeline, and the LogNNet reservoir-computing classifier. Using four commands from the Speech Commands da-taset downsampled to 8 kHz, we evaluate four MFCC aggregation schemes and find that adaptive binning (64-dimensional feature vector) offers the best accuracy-to-compactness trade-off. The LogNNet classifier with architecture 64:33:9:4 reaches 92.04% accuracy under speaker-independent evaluation, while requiring significantly fewer parameters than conventional deep learn-ing models. Hardware implementation on Arduino Nano 33 IoT (ARM Cor-tex-M0+, 48 MHz, 32 KB RAM) validates the practical feasibility, achieving ~90% real-time recognition accuracy while consuming only 18 KB RAM (55% utilization). The complete pipeline (VAD -> MFCC -> LogNNet) thus enables reliable on-device speech-command recognition under strict memory and compute limits, making it suitable for battery-powered IoT nodes, wire-less sensor networks, and hands-free control interfaces.