  Keyword Spotting (KWS) from speech signals is widely applied to perform fully
hands-free speech recognition. The KWS network is designed as a small-footprint
model so it can continuously be active. Recent efforts have explored dynamic
filter-based models in deep learning frameworks to enhance the system's
robustness or accuracy. However, as a dynamic filter framework requires high
computational costs, the implementation is limited to the computational
condition of the device. In this paper, we propose a lightweight dynamic filter
to improve the performance of KWS. Our proposed model divides the dynamic
filter into two branches to reduce computational complexity: pixel level and
instance level. The proposed lightweight dynamic filter is applied to the front
end of KWS to enhance the separability of the input data. The experimental
results show that our model is robustly working on unseen noise and small
training data environments by using a small computational resource.
