Efficient audio feature extraction is critical for low-latency, resource-constrained speech recognition. Conventional preprocessing techniques, such as Mel Spectrogram, Perceptual Linear Prediction (PLP), and Learnable Spectrogram, achieve high classification accuracy but require large feature sets and significant computation. The low-latency and power efficiency benefits of neuromorphic computing offer a strong potential for audio classification. Here, we introduce memristive nanowire networks as a neuromorphic hardware preprocessing layer for spoken-digit classification, a capability not previously demonstrated. Nanowire networks extract compact, informative features directly from raw audio, achieving a favorable trade-off between accuracy, dimensionality reduction from the original audio size (data compression) , and training time efficiency. Compared with state-of-the-art software techniques, nanowire features reach 98.95% accuracy with 66 times data compression (XGBoost) and 97.9% accuracy with 255 times compression (Random Forest) in sub-second training latency. Across multiple classifiers nanowire features consistently achieve more than 90% accuracy with more than 62.5 times compression, outperforming features extracted by conventional state-of-the-art techniques such as MFCC in efficiency without loss of performance. Moreover, nanowire features achieve 96.5% accuracy classifying multispeaker audios, outperforming all state-of-the-art feature accuracies while achieving the highest data compression and lowest training time. Nanowire network preprocessing also enhances linear separability of audio data, improving simple classifier performance and generalizing across speakers. These results demonstrate that memristive nanowire networks provide a novel, low-latency, and data-efficient feature extraction approach, enabling high-performance neuromorphic audio classification.