  Deep neural networks have recently become a popular solution to keyword
spotting systems, which enable the control of smart devices via voice. In this
paper, we apply neural architecture search to search for convolutional neural
network models that can help boost the performance of keyword spotting based on
features extracted from acoustic signals while maintaining an acceptable memory
footprint. Specifically, we use differentiable architecture search techniques
to search for operators and their connections in a predefined cell search
space. The found cells are then scaled up in both depth and width to achieve
competitive performance. We evaluated the proposed method on Google's Speech
Commands Dataset and achieved a state-of-the-art accuracy of over 97% on the
setting of 12-class utterance classification commonly reported in the
literature.
