  In this paper, we propose a fully-neural approach to open-vocabulary keyword
spotting, that allows the users to include a customizable voice interface to
their device and that does not require task-specific data. We present a keyword
detection neural network weighing less than 250KB, in which the topmost layer
performing keyword detection is predicted by an auxiliary network, that may be
run offline to generate a detector for any keyword. We show that the proposed
model outperforms acoustic keyword spotting baselines by a large margin on two
tasks of detecting keywords in utterances and three tasks of detecting isolated
speech commands. We also propose a method to fine-tune the model when specific
training data is available for some keywords, which yields a performance
similar to a standard speech command neural network while keeping the ability
of the model to be applied to new keywords.
