  Keyword Spotting (KWS) enables speech-based user interaction on smart
devices. Always-on and battery-powered application scenarios for smart devices
put constraints on hardware resources and power consumption, while also
demanding high accuracy as well as real-time capability. Previous architectures
first extracted acoustic features and then applied a neural network to classify
keyword probabilities, optimizing towards memory footprint and execution time.
Compared to previous publications, we took additional steps to reduce power and
memory consumption without reducing classification accuracy. Power-consuming
audio preprocessing and data transfer steps are eliminated by directly
classifying from raw audio. For this, our end-to-end architecture extracts
spectral features using parametrized Sinc-convolutions. Its memory footprint is
further reduced by grouping depthwise separable convolutions. Our network
achieves the competitive accuracy of 96.4% on Google's Speech Commands test set
with only 62k parameters.
