标题： 基于LogNNet的嵌入式系统语音命令识别 显示英文标题

标题： Speech Command Recognition Using LogNNet Reservoir Computing for Embedded Systems

摘要： 本文提出了一种低资源语音命令识别器，结合了基于能量的语音活动检测（VAD）、优化的梅尔频率倒谱系数（MFCC）处理流程以及LogNNet水库计算分类器。 使用来自语音命令数据集的四个命令，下采样至8 kHz，我们评估了四种MFCC聚合方案，并发现自适应分箱（64维特征向量）在准确率与紧凑性之间提供了最佳平衡。 具有架构64:33:9:4的LogNNet分类器在说话人独立评估下达到92.04%的准确率，同时所需的参数显著少于传统深度学习模型。 在Arduino Nano 33 IoT（ARM Cortex-M0+，48 MHz，32 KB RAM）上的硬件实现验证了实际可行性，在仅消耗18 KB RAM（55%利用率）的情况下实现了约90%的实时识别准确率。 因此，完整的流程（VAD -> MFCC -> LogNNet）在严格的内存和计算限制下实现了可靠的设备端语音命令识别，使其适用于电池供电的物联网节点、无线传感器网络和免提控制界面。 显示英文摘要

摘要： 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.