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

arXiv: https://arxiv.org/abs/2008.08113 · HTML: https://mlsanity.com/paper/2008.08113/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2020.

## Built on by (most cited first, 4 of 4)

- [Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation](https://mlsanity.com/paper/2105.06598/) (2021)
- [Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR](https://mlsanity.com/paper/2210.12134/) (2022)
- [Device-Directed Speech Detection for Follow-up Conversations Using Large Language Models](https://mlsanity.com/paper/2411.00023/) (2024)
- [Not All Speech Is Intent: Adaptive Self-Correcting Inference Layer for Post-ASR False Wake-Up](https://mlsanity.com/paper/2609.12469/) (2026)

## Abstract (arXiv)

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%.