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Keyword Spotting (KWS) enables speech-based user interaction on smartdevices. Always-on and battery-powered application scenarios for smart devicesput constraints on hardware resources and power consumption, while alsodemanding high accuracy as well as real-time capability. Previous architecturesfirst extracted acoustic features and then applied a neural network to classifykeyword probabilities, optimizing towards memory footprint and execution time.Compared to previous publications, we took additional steps to reduce power andmemory consumption without reducing classification accuracy. Power-consumingaudio preprocessing and data transfer steps are eliminated by directlyclassifying from raw audio. For this, our end-to-end architecture extractsspectral features using parametrized Sinc-convolutions. Its memory footprint isfurther reduced by grouping depthwise separable convolutions. Our networkachieves the competitive accuracy of 96.4% on Google's Speech Commands test setwith only 62k parameters.

Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions