KFA: Keyword Feature Augmentation for Open Set Keyword Spotting

Abstract

In recent years, with the advancement of deep learning technology and the emergence of smart devices, there has been a growing interest in keyword spotting (KWS), which is used to activate AI systems with automatic speech recognition and text-to-speech. However, smart devices with KWS often encounter false alarm errors when inputting unexpected words. To address this issue, existing KWS methods typically train non-target words as an unknown class. Despite these efforts, there is still a possibility that unseen words not trained as part of the unknown class could be misclassified as one of the target words. To overcome this limitation, we propose a new method named Keyword Feature Augmentation (KFA) for open-set KWS. KFA performs feature augmentation through adversarial learning to increase the loss. The augmented features are constrained within a limited space using label smoothing. Unlike other generative model-based open set recognition (OSR) methods, KFA does not require any additional training parameters or repeated operation for inference. As a result, KFA has achieved a 0.955 AUROC score and 97.34% target class accuracy for Google Speech Commands V1, and a 0.959 AUROC score and 98.17% target class accuracy for Google Speech Commands V2, which is the highest performance when compared to various OSR methods.