Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation

• URL: http://arxiv.org/abs/2008.08113v1
• Date: Tue, 18 Aug 2020 18:21:33 GMT
• Title: Complementary Language Model and Parallel Bi-LRNN for False Trigger Mitigation
• Authors: Rishika Agarwal, Xiaochuan Niu, Pranay Dighe, Srikanth Vishnubhotla, Sameer Badaskar, Devang Naik
• Abstract summary: False trigger mitigation (FTM) is a process to detect the false trigger events and respond appropriately to the user. We propose a novel solution by introducing a parallel ASR decoding process with a special language model trained from "out-of-domain" data sources.
• Score: 9.960986677222358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 $10.8\%$.

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