Wake word detection exists in most intelligent homes and portable devices. It
offers these devices the ability to "wake up" when summoned at a low cost of
power and computing. This paper focuses on understanding alignment's role in
developing a wake-word system that answers a generic phrase. We discuss three
approaches. The first is alignment-based, where the model is trained with
frame-wise cross-entropy. The second is alignment-free, where the model is
trained with CTC. The third, proposed by us, is a hybrid solution in which the
model is trained with a small set of aligned data and then tuned with a
sizeable unaligned dataset. We compare the three approaches and evaluate the
impact of the different aligned-to-unaligned ratios for hybrid training. Our
results show that the alignment-free system performs better alignment-based for
the target operating point, and with a small fraction of the data (20%), we can
train a model that complies with our initial constraints.

本文研究唤醒词系统中对齐方法在开发智能家居和便携设备中的应用。我们提出了三种方法，包括基于对齐、无对齐和混合方法。我们发现无对齐系统在目标操作点上表现更好，并且只需使用少量数据即可训练符合初始约束条件的模型（20%）。