  We describe the design of a voice trigger detection system for smart
speakers. In this study, we address two major challenges. The first is that the
detectors are deployed in complex acoustic environments with external noise and
loud playback by the device itself. Secondly, collecting training examples for
a specific keyword or trigger phrase is challenging resulting in a scarcity of
trigger phrase specific training data. We describe a two-stage cascaded
architecture where a low-power detector is always running and listening for the
trigger phrase. If a detection is made at this stage, the candidate audio
segment is re-scored by larger, more complex models to verify that the segment
contains the trigger phrase. In this study, we focus our attention on the
architecture and design of these second-pass detectors. We start by training a
general acoustic model that produces phonetic transcriptions given a large
labelled training dataset. Next, we collect a much smaller dataset of examples
that are challenging for the baseline system. We then use multi-task learning
to train a model to simultaneously produce accurate phonetic transcriptions on
the larger dataset \emph{and} discriminate between true and easily confusable
examples using the smaller dataset. Our results demonstrate that the proposed
model reduces errors by half compared to the baseline in a range of challenging
test conditions \emph{without} requiring extra parameters.
