  We consider the design of two-pass voice trigger detection systems. We focus
on the networks in the second pass that are used to re-score candidate segments
obtained from the first-pass. Our baseline is an acoustic model(AM), with
BiLSTM layers, trained by minimizing the CTC loss. We replace the BiLSTM layers
with self-attention layers. Results on internal evaluation sets show that
self-attention networks yield better accuracy while requiring fewer parameters.
We add an auto-regressive decoder network on top of the self-attention layers
and jointly minimize the CTC loss on the encoder and the cross-entropy loss on
the decoder. This design yields further improvements over the baseline. We
retrain all the models above in a multi-task learning(MTL) setting, where one
branch of a shared network is trained as an AM, while the second branch
classifies the whole sequence to be true-trigger or not. Results demonstrate
that networks with self-attention layers yield $\sim$60% relative reduction in
false reject rates for a given false-alarm rate, while requiring 10% fewer
parameters. When trained in the MTL setup, self-attention networks yield
further accuracy improvements. On-device measurements show that we observe 70%
relative reduction in inference time. Additionally, the proposed network
architectures are $\sim$5X faster to train.
