We explore techniques to improve the robustness of small-footprint keyword spotting
models based on deep neural networks (DNNs) in the presence of background noise and
in far-field conditions. We find that system performance can be improved
significantly, with relative improvements up to 75% in far-field conditions, by
employing a combination of multi-style training and a proposed novel formulation of
automatic gain control (AGC) that estimates the levels of both speech and
background noise. Further, we find that these techniques allow us to achieve
competitive performance, even when applied to DNNs with an order of magnitude fewer
parameters than our baseline.