  Despite the recent successes of deep neural networks, it remains challenging
to achieve high precision keyword spotting task (KWS) on resource-constrained
devices. In this study, we propose a novel context-aware and compact
architecture for keyword spotting task. Based on residual connection and
bottleneck structure, we design a compact and efficient network for KWS task.
To leverage the long range dependencies and global context of the convolutional
feature maps, the graph convolutional network is introduced to encode the
non-local relations. By evaluated on the Google Speech Command Dataset, the
proposed method achieves state-of-the-art performance and outperforms the prior
works by a large margin with lower computational cost.
