  In this paper, we propose a multilingual query-by-example keyword spotting
(KWS) system based on a residual neural network. The model is trained as a
classifier on a multilingual keyword dataset extracted from Common Voice
sentences and fine-tuned using circle loss. We demonstrate the generalization
ability of the model to new languages and report a mean reduction in EER of
59.2 % for previously seen and 47.9 % for unseen languages compared to a
competitive baseline. We show that the word embeddings learned by the KWS model
can be accurately predicted from the phoneme sequences using a simple LSTM
model. Our system achieves a promising accuracy for streaming keyword spotting
and keyword search on Common Voice audio using just 5 examples per keyword.
Experiments on the Hey-Snips dataset show a good performance with a false
negative rate of 5.4 % at only 0.1 false alarms per hour.
