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

Hybrid Transformer/CTC Networks for Hardware Efficient Voice Triggering