  Modern approaches for keyword spotting rely on training deep neural networks
on large static datasets with i.i.d. distributions. However, the resulting
models tend to underperform when presented with changing data regimes in
real-life applications. This work investigates a simple but effective online
continual learning method that updates a keyword spotter on-device via SGD as
new data becomes available. Contrary to previous research, this work focuses on
learning the same KWS task, which covers most commercial applications. During
experiments with dynamic audio streams in different scenarios, that method
improves the performance of a pre-trained small-footprint model by 34%.
Moreover, experiments demonstrate that, compared to a naive online learning
implementation, conditional model updates based on its performance in a small
hold-out set drawn from the training distribution mitigate catastrophic
forgetting.
