Certainty-Guided Feature Separation for Early Decision in Spiking Keyword Spotting

Keyword spotting (KWS) requires low latency, low energy consumption, and high recognition accuracy, making efficient early decision-making particularly important for resource-constrained edge devices. Existing early-decision methods for spiking neural networks (SNNs) mainly supervise cumulative outputs across time steps, while feature-level approaches often enforce uniform temporal separation and overlook the progressively increasing discriminative information in speech. In this work, we propose a certainty-guided spiking feature separation framework that dynamically estimates the informativeness of each time step from the SNN's predictive distribution and uses it to weight intermediate feature separation. By applying a certainty-guided feature separation objective combined with a dynamic training schedule, unreliable separation are suppressed during early training and discriminative representation learning are strengthened as the classifier becomes more reliable. Experiments on GSC V2 and MSWC Microset datasets demonstrate that our method improves early decision accuracy while maintaining or improving final recognition accuracy, achieving better performance across multiple time steps. The proposed framework provides a lightweight and inference-free approach to learning temporally adaptive spiking representations, offering a promising direction for low-latency and energy-efficient KWS on edge devices.