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Wake word detection exists in most intelligent homes and portable devices. Itoffers these devices the ability to "wake up" when summoned at a low cost ofpower and computing. This paper focuses on understanding alignment's role indeveloping a wake-word system that answers a generic phrase. We discuss threeapproaches. The first is alignment-based, where the model is trained withframe-wise cross-entropy. The second is alignment-free, where the model istrained with CTC. The third, proposed by us, is a hybrid solution in which themodel is trained with a small set of aligned data and then tuned with asizeable unaligned dataset. We compare the three approaches and evaluate theimpact of the different aligned-to-unaligned ratios for hybrid training. Ourresults show that the alignment-free system performs better than thealignment-based for the target operating point, and with a small fraction ofthe data (20%), we can train a model that complies with our initialconstraints.

Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches