Predicting detection filters for small footprint open-vocabulary keyword spotting
This enables small-footprint, customizable keyword detection for devices, but it is incremental as it builds on existing neural methods with specific optimizations.
The paper tackles the problem of open-vocabulary keyword spotting for customizable voice interfaces without task-specific data, proposing a neural network under 250KB that predicts detection filters and outperforms baselines by a large margin on multiple tasks.
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.