Virtual Adversarial Training for DS-CNN Based Small-Footprint Keyword Spotting

Abstract

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 specifically-generated 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.