  Keyword spotting (KWS) on mobile devices generally requires a small memory
footprint. However, most current models still maintain a large number of
parameters in order to ensure good performance. In this paper, we propose a
temporally pooled attention module which can capture global features better
than the AveragePool. Besides, we design a separable temporal convolution
network which leverages depthwise separable and temporal convolution to reduce
the number of parameter and calculations. Finally, taking advantage of
separable temporal convolution and temporally pooled attention, a efficient
neural network (ST-AttNet) is designed for KWS system. We evaluate the models
on the publicly available Google speech commands data sets V1. The number of
parameters of proposed model (48K) is 1/6 of state-of-the-art TC-ResNet14-1.5
model (305K). The proposed model achieves a 96.6% accuracy, which is comparable
to the TC-ResNet14-1.5 model (96.6%).
