  This paper proposes a neural network architecture for tackling the
query-by-example user-defined keyword spotting task. A multi-head attention
module is added on top of a multi-layered GRU for effective feature extraction,
and a normalized multi-head attention module is proposed for feature
aggregation. We also adopt the softtriple loss - a combination of triplet loss
and softmax loss - and showcase its effectiveness. We demonstrate the
performance of our model on internal datasets with different languages and the
public Hey-Snips dataset. We compare the performance of our model to a baseline
system and conduct an ablation study to show the benefit of each component in
our architecture. The proposed work shows solid performance while preserving
simplicity.
