  Keyword spotting aims to identify specific keyword audio utterances. In
recent years, deep convolutional neural networks have been widely utilized in
keyword spotting systems. However, their model architectures are mainly based
on off-the shelfbackbones such as VGG-Net or ResNet, instead of specially
designed for the task. In this paper, we utilize neural architecture search to
design convolutional neural network models that can boost the performance of
keyword spotting while maintaining an acceptable memory footprint.
Specifically, we search the model operators and their connections in a specific
search space with Encoder-Decoder neural architecture optimization. Extensive
evaluations on Google's Speech Commands Dataset show that the model
architecture searched by our approach achieves a state-of-the-art accuracy of
over 97%.
