# Lattice-based Improvements for Voice Triggering Using Graph Neural Networks

arXiv: https://arxiv.org/abs/2001.10822 · HTML: https://mlsanity.com/paper/2001.10822/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jan 2020.

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

- [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)
- [wav2graph: A Framework for Supervised Learning Knowledge Graph from Speech](https://mlsanity.com/paper/2408.04174/) (2024)
- [GCoDE: Efficient Device-Edge Co-Inference for GNNs via Architecture-Mapping Co-Search](https://mlsanity.com/paper/2512.11856/) (2025)

## Abstract (arXiv)

Voice-triggered smart assistants often rely on detection of a trigger-phrase before they start listening for the user request. Mitigation of false triggers is an important aspect of building a privacy-centric non-intrusive smart assistant. In this paper, we address the task of false trigger mitigation (FTM) using a novel approach based on analyzing automatic speech recognition (ASR) lattices using graph neural networks (GNN). The proposed approach uses the fact that decoding lattice of a falsely triggered audio exhibits uncertainties in terms of many alternative paths and unexpected words on the lattice arcs as compared to the lattice of a correctly triggered audio. A pure trigger-phrase detector model doesn't fully utilize the intent of the user speech whereas by using the complete decoding lattice of user audio, we can effectively mitigate speech not intended for the smart assistant. We deploy two variants of GNNs in this paper based on 1) graph convolution layers and 2) self-attention mechanism respectively. Our experiments demonstrate that GNNs are highly accurate in FTM task by mitigating ~87% of false triggers at 99% true positive rate (TPR). Furthermore, the proposed models are fast to train and efficient in parameter requirements.