Keyword Spotting (KWS) is essential in edge computing requiring rapid and
energy-efficient responses. Spiking Neural Networks (SNNs) are well-suited for
KWS for their efficiency and temporal capacity for speech. To further reduce
the latency and energy consumption, this study introduces ED-sKWS, an SNN-based
KWS model with an early-decision mechanism that can stop speech processing and
output the result before the end of speech utterance. Furthermore, we introduce
a Cumulative Temporal (CT) loss that can enhance prediction accuracy at both
the intermediate and final timesteps. To evaluate early-decision performance,
we present the SC-100 dataset including 100 speech commands with beginning and
end timestamp annotation. Experiments on the Google Speech Commands v2 and our
SC-100 datasets show that ED-sKWS maintains competitive accuracy with 61%
timesteps and 52% energy consumption compared to SNN models without
early-decision mechanism, ensuring rapid response and energy efficiency.

该研究介绍了一种基于脉冲神经网络的关键词检测模型 ED-sKWS，该模型引入了一种早期决策机制，能够在语音结束前停止语音处理并输出结果。此外，该研究还引入了一种累积时间损失 CT，可提高预测精度。实验证明，相比没有早期决策机制的脉冲神经网络模型，ED-sKWS 在 61% 时间步长和 52% 能量消耗方面具有竞争力，保证了快速响应和能量效率。