In many scenarios, detecting keywords from natural language queries is sufficient to understand the intent of the user. In this paper, we propose a fully-neural approach to open-vocabulary keyword spotting, allowing a user to include a  voice interface to its device without having to retrain a model on task-specific data. We present a keyword detection neural network weighing less than 550KB , 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  .
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 .
D
many scenarios, detecting keywords from natural language queries is sufficient to understand the intent of the user. In
R
allowing a user
that allows the users
A
customizable
R
its device without having to retrain a model on
their device and that does not require
R
550KB
250KB
A
. 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