  We propose a method to reduce false voice triggers of a speech-enabled
personal assistant by post-processing the hypothesis lattice of a server-side
large-vocabulary continuous speech recognizer (LVCSR) via a neural network. We
first discuss how an estimate of the posterior probability of the trigger
phrase can be obtained from the hypothesis lattice using known techniques to
perform detection, then investigate a statistical model that processes the
lattice in a more explicitly data-driven, discriminative manner. We propose
using a Bidirectional Lattice Recurrent Neural Network (LatticeRNN) for the
task, and show that it can significantly improve detection accuracy over using
the 1-best result or the posterior.
