VIRTUAL ADVERSARIAL TRAINING FOR DS-CNN BASED SMALL-FOOTPRINT KEYWORD SPOTTING

Serving as the tigger of a voice-enabled user interface, on-device keyword spotting model has to be extremely compact, efficient and accurate. In this paper, we adopt a depth-wise separable convolutional neural network (DS-CNN) as our small-footprint KWS model, which is highly competitive to these ends. However, recent study has shown that a compact KWS system is very vulnerable to small adversarial perturbations while augmenting the training data with specificallygenerated adversarial examples can improve performance. In this paper, we further improve KWS performance through a virtual adversarial training (VAT) solution. Instead of using adversarial examples for data augmentation, we propose to train a DS-CNN KWS model using adversarial regularization, which aims to smooth model's distribution and thus to improve robustness, by explicitly introducing a distribution smoothness measure into the loss function. Experiments on a collected KWS corpus using a circular microphone array in far-field scenario show that the VAT approach brings 31.9% relative false rejection rate (FRR) reduction compared to the normal training approach with cross entropy loss, and it also surpasses the adversarial example based data augmentation approach with 10.3% relative FRR reduction.