Keyword spotting systems continuously process audio streams to detect
keywords. One of the most challenging tasks in designing such systems is to
reduce False Alarm (FA) which happens when the system falsely registers a
keyword despite the keyword not being uttered. In this paper, we propose a
simple yet elegant solution to this problem that follows from the law of total
probability. We show that existing deep keyword spotting mechanisms can be
improved by Successive Refinement, where the system first classifies whether
the input audio is speech or not, followed by whether the input is keyword-like
or not, and finally classifies which keyword was uttered. We show across
multiple models with size ranging from 13K parameters to 2.41M parameters, the
successive refinement technique reduces FA by up to a factor of 8 on in-domain
held-out FA data, and up to a factor of 7 on out-of-domain (OOD) FA data.
Further, our proposed approach is "plug-and-play" and can be applied to any
deep keyword spotting model.

该研究提出了一种基于 Successive Refinement 的简单而优美的解决方案，旨在降低关键词检测系统中的误报率，并表明无论模型的大小如何，该技术都能将误报率降低最多 8 倍，并且可以应用于任何深度关键字检测模型。