  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 than the
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.
