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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 aclassifier on a multilingual keyword dataset extracted from Common Voicesentences and fine-tuned using circle loss. We demonstrate the generalizationability of the model to new languages and report a mean reduction in EER of59.2 % for previously seen and 47.9 % for unseen languages compared to acompetitive baseline. We show that the word embeddings learned by the KWS modelcan be accurately predicted from the phoneme sequences using a simple LSTMmodel. Our system achieves a promising accuracy for streaming keyword spottingand keyword search on Common Voice audio using just 5 examples per keyword.Experiments on the Hey-Snips dataset show a good performance with a falsenegative rate of 5.4 % at only 0.1 false alarms per hour.

Multilingual Query-by-Example Keyword Spotting with Metric Learning and Phoneme-to-Embedding Mapping