# Voice trigger detection from LVCSR hypothesis lattices using bidirectional lattice recurrent neural networks

arXiv: https://arxiv.org/abs/2003.00304 · HTML: https://mlsanity.com/paper/2003.00304/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 7 of 7)

- [Leveraging Large Language Models for Exploiting ASR Uncertainty](https://mlsanity.com/paper/2309.04842/) (2023)
- [Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation](https://mlsanity.com/paper/2105.06598/) (2021)
- [SELMA: A Speech-Enabled Language Model for Virtual Assistant Interactions](https://mlsanity.com/paper/2501.19377/) (2025)
- [Device-Directed Speech Detection: Regularization via Distillation for Weakly-Supervised Models](https://mlsanity.com/paper/2203.15975/) (2022)
- [Audio-to-Intent Using Acoustic-Textual Subword Representations from End-to-End ASR](https://mlsanity.com/paper/2210.12134/) (2022)
- [Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features](https://mlsanity.com/paper/2310.15261/) (2023)
- [Using Pause Information for More Accurate Entity Recognition](https://mlsanity.com/paper/2109.13222/) (2021)

## Abstract (arXiv)

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.