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