  We present an architecture for voice trigger detection for virtual
assistants. The main idea in this work is to exploit information in words that
immediately follow the trigger phrase. We first demonstrate that by including
more audio context after a detected trigger phrase, we can indeed get a more
accurate decision. However, waiting to listen to more audio each time incurs a
latency increase. Progressive Voice Trigger Detection allows us to trade-off
latency and accuracy by accepting clear trigger candidates quickly, but waiting
for more context to decide whether to accept more marginal examples. Using a
two-stage architecture, we show that by delaying the decision for just 3% of
detected true triggers in the test set, we are able to obtain a relative
improvement of 66% in false rejection rate, while incurring only a negligible
increase in latency.
