Small-Footprint Keyword Spotting with Graph Convolutional Network
Xi Chen, Shouyi Yin, Dandan Song, Peng Ouyang, Leibo Liu, Shaojun Wei · 2019 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) · 2019
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 nonlocal 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.