Deep Keyword Spotting (KWS) systems continuously process audio streams to detect keywords. However, performance of deep neural networks degrade when the input data diverges from the training data; referred to as Out-of-Distribution (OOD) data problem. In this paper, we show performance degradation of existing State-of-the-Art (SOTA) keyword spotting models on OOD data w.r.t. in-domain testing data, and propose a training mechanism to improve performance on OOD data. Specifically, we propose a novel combination of Mixup and Information Bottleneck, called MIB, to achieve SOTA performance on OOD data. Considering on-device applications, we show across multiple models ranging from sizes of 12.5K parameters to 350K parameters, that MIB achieves as much as 2.5% (absolute) improvement in performance over OOD data. Further, in the more realistic case where OOD keywords are uttered in the presence of OOD noise, MIB achieves as much as 10% (absolute) performance improvement over SOTA models. The proposed MIB is model-agnostic, i.e., it can be applied to enhance the training of any deep keyword spotting model.

https://ieeexplore.ieee.org/document/10889347