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。

是否唤醒：通过连续细化减少关键字误报

To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive  Refinement

We propose deep learning based communication methods for adaptive-bandwidth
transmission of images over wireless channels. We consider the scenario in
which images are transmitted progressively in layers over time or frequency,
and such layers can be aggregated by receivers in order to increase the quality
of their reconstructions. We investigate two scenarios, one in which the layers
are sent sequentially, and incrementally contribute to the refinement of a
reconstruction, and another in which the layers are independent and can be
retrieved in any order. Those scenarios correspond to the well known problems
of \textit{successive refinement} and \textit{multiple descriptions},
respectively, in the context of joint source-channel coding (JSCC). We propose
DeepJSCC-$l$, an innovative solution that uses convolutional autoencoders, and
present three architectures with different complexity trade-offs. To the best
of our knowledge, this is the first practical multiple-description JSCC scheme
developed and tested for practical information sources and channels. Numerical
results show that DeepJSCC-$l$ can learn to transmit the source progressively
with negligible losses in the end-to-end performance compared with a single
transmission. Moreover, DeepJSCC-$l$ has comparable performance with state of
the art digital progressive transmission schemes in the challenging low
signal-to-noise ratio (SNR) and small bandwidth regimes, with the additional
advantage of graceful degradation with channel SNR.

通过使用基于深度学习的通信方法，我们提出了一个用于适应带宽传输图像的方案，并考虑由接收器聚合以提高重建质量的图像逐层传输的情形。我们提出了 DeepJSCC-l，这是第一个为实际信息源和信道开发和测试的多描述 JSCC 方案，并具有与数字渐进传输方案相媲美的性能。