论文标题
生成对抗样本，以训练唤醒单词检测系统，以免混淆单词
Generating Adversarial Samples For Training Wake-up Word Detection Systems Against Confusing Words
论文作者
论文摘要
唤醒单词检测模型在现实生活中被广泛使用，但是在遇到对抗样本时会遭受严重的性能降解。在本文中，我们讨论了在对抗样本中使单词混淆的概念。令人困惑的单词通常会遇到，这些单词听起来与预定义的关键字相似。为了增强唤醒单词检测系统的鲁棒性，我们提出了几种方法来生成对抗性混乱的样本，以模拟真正令人困惑的单词场景，在这种情况下，我们通常在训练集中没有任何真正令人困惑的样本。生成的样本包括串联音频，合成数据和部分掩盖的关键字。此外，我们使用嵌入串联系统的域来提高性能。实验结果表明，在我们的方法中产生的对抗样本有助于在常见场景和令人困惑的单词场景中改善系统的鲁棒性。此外，我们发布了令人困惑的单词测试数据库，称为HI-MIA-CW，用于未来的研究。
Wake-up word detection models are widely used in real life, but suffer from severe performance degradation when encountering adversarial samples. In this paper we discuss the concept of confusing words in adversarial samples. Confusing words are commonly encountered, which are various kinds of words that sound similar to the predefined keywords. To enhance the wake word detection system's robustness against confusing words, we propose several methods to generate the adversarial confusing samples for simulating real confusing words scenarios in which we usually do not have any real confusing samples in the training set. The generated samples include concatenated audio, synthesized data, and partially masked keywords. Moreover, we use a domain embedding concatenated system to improve the performance. Experimental results show that the adversarial samples generated in our approach help improve the system's robustness in both the common scenario and the confusing words scenario. In addition, we release the confusing words testing database called HI-MIA-CW for future research.
