Speech recognition is a key challenge in natural language processing, requiring low latency, efficient computation, and strong generalization for real-time applications. While software-based artificial neural networks (ANNs) excel at this task, they are computationally intensive and depend heavily on data pre-processing. Neuromorphic computing, with its low-latency and energy-efficient advantages, holds promise for audio classification. Memristive nanowire networks, combined with pre-processing techniques like Mel-Frequency Cepstrum Coefficient extraction, have been widely used for associative learning, but such pre-processing can be power-intensive, undermining latency benefits. This study pioneers the use of memristive and spatio-temporal properties of nanowire networks for audio signal classification without pre-processing. A nanowire network simulation is paired with three linear classifiers for 10-class MNIST audio classification and binary speaker generalization tests. The hybrid system achieves significant benefits: excellent data compression with only 3% of nanowire output utilized, a 10-fold reduction in computational latency, and up to 28.5% improved classification accuracy (using a logistic regression classifier). Precision and recall improve by 10% and 17% for multispeaker datasets, and by 24% and 17% for individual speaker datasets, compared to raw data classifiers.This work provides a foundational proof of concept for utilizing memristive nanowire networks (NWN) in edge-computing devices, showcasing their potential for efficient, real-time audio signal processing with reduced computational overhead and power consumption, and enabling the development of advanced neuromorphic computing solutions.

翻译：语音识别是自然语言处理中的关键挑战，实时应用需要低延迟、高效计算和强泛化能力。尽管基于软件的人工神经网络在此任务中表现出色，但其计算密集且严重依赖数据预处理。神经形态计算凭借其低延迟和能效优势，在音频分类领域展现出潜力。忆阻纳米线网络结合梅尔频率倒谱系数提取等预处理技术已广泛用于联想学习，但此类预处理可能功耗较高，削弱了延迟优势。本研究开创性地利用纳米线网络的忆阻与时空特性实现无需预处理的音频信号分类。通过将纳米线网络仿真与三种线性分类器结合，在10类MNIST音频分类和二元说话人泛化测试中验证性能。该混合系统取得显著优势：仅使用3%的纳米线输出即可实现优异的数据压缩，计算延迟降低10倍，分类准确率最高提升28.5%（使用逻辑回归分类器）。与原始数据分类器相比，多说话人数据集的精确率和召回率分别提升10%与17%，单说话人数据集分别提升24%与17%。本工作为在边缘计算设备中应用忆阻纳米线网络提供了基础概念验证，展示了其在降低计算开销与功耗的同时实现高效实时音频信号处理的潜力，为先进神经形态计算解决方案的开发奠定基础。