  We present a unified and hardware efficient architecture for two stage voice
trigger detection (VTD) and false trigger mitigation (FTM) tasks. Two stage VTD
systems of voice assistants can get falsely activated to audio segments
acoustically similar to the trigger phrase of interest. FTM systems cancel such
activations by using post trigger audio context. Traditional FTM systems rely
on automatic speech recognition lattices which are computationally expensive to
obtain on device. We propose a streaming transformer (TF) encoder architecture,
which progressively processes incoming audio chunks and maintains audio context
to perform both VTD and FTM tasks using only acoustic features. The proposed
joint model yields an average 18% relative reduction in false reject rate (FRR)
for the VTD task at a given false alarm rate. Moreover, our model suppresses
95% of the false triggers with an additional one second of post-trigger audio.
Finally, on-device measurements show 32% reduction in runtime memory and 56%
reduction in inference time compared to non-streaming version of the model.
