  False triggers in voice assistants are unintended invocations of the
assistant, which not only degrade the user experience but may also compromise
privacy. False trigger mitigation (FTM) is a process to detect the false
trigger events and respond appropriately to the user. In this paper, we propose
a novel solution to the FTM problem by introducing a parallel ASR decoding
process with a special language model trained from "out-of-domain" data
sources. Such language model is complementary to the existing language model
optimized for the assistant task. A bidirectional lattice RNN (Bi-LRNN)
classifier trained from the lattices generated by the complementary language
model shows a $38.34\%$ relative reduction of the false trigger (FT) rate at
the fixed rate of $0.4\%$ false suppression (FS) of correct invocations,
compared to the current Bi-LRNN model. In addition, we propose to train a
parallel Bi-LRNN model based on the decoding lattices from both language
models, and examine various ways of implementation. The resulting model leads
to further reduction in the false trigger rate by $10.8\%$.
