Previous research methods on wake-up word detection (WWD) have been proposed with focus on finding a decent word representation that can well express the characteristics of a word. However, there are various obstacles such as noise and reverberation which make it difficult in real-world environments where WWD works. To tackle this, we propose a novel architecture called interlayer selective attention network (ISAN) which generates more robust word representation by introducing the concept of selective attention. Experiments in real-world scenarios demonstrated that the proposed ISAN outperformed several baseline methods as well as other attention methods. In addition, the effectiveness of ISAN was analyzed with visualizations.

中文翻译：

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用于鲁棒个性化唤醒词检测的层间选择性注意网络

之前已经提出了关于唤醒词检测（WWD）的研究方法，重点是寻找能够很好地表达单词特征的合适的词表示。但是，存在各种障碍，例如噪音和混响，这使得在 WWD 工作的现实环境中变得困难。为了解决这个问题，我们提出了一种称为层间选择性注意网络（ISAN）的新架构，它通过引入选择性注意的概念来生成更健壮的词表示。现实世界场景中的实验表明，所提出的 ISAN 优于几种基线方法以及其他注意力方法。此外，还通过可视化分析了 ISAN 的有效性。