  This paper introduces neural architecture search (NAS) for the automatic
discovery of end-to-end keyword spotting (KWS) models in limited resource
environments. We employ a differentiable NAS approach to optimize the structure
of convolutional neural networks (CNNs) operating on raw audio waveforms. After
a suitable KWS model is found with NAS, we conduct quantization of weights and
activations to reduce the memory footprint. We conduct extensive experiments on
the Google speech commands dataset. In particular, we compare our end-to-end
approach to mel-frequency cepstral coefficient (MFCC) based systems. For
quantization, we compare fixed bit-width quantization and trained bit-width
quantization. Using NAS only, we were able to obtain a highly efficient model
with an accuracy of 95.55% using 75.7k parameters and 13.6M operations. Using
trained bit-width quantization, the same model achieves a test accuracy of
93.76% while using on average only 2.91 bits per activation and 2.51 bits per
weight.
