# To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement

arXiv: https://arxiv.org/abs/2304.03416 · HTML: https://mlsanity.com/paper/2304.03416/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 6 Apr 2023.

## Builds on (most cited first, 21 of 21)

- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [MUSAN: A Music, Speech, and Noise Corpus](https://mlsanity.com/paper/1510.08484/) (2015)
- [Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separation](https://mlsanity.com/paper/1804.03619/) (2018)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Keyword Transformer: A Self-Attention Model for Keyword Spotting](https://mlsanity.com/paper/2104.00769/) (2021)
- [Temporal Convolution for Real-time Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1904.03814/) (2019)
- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Streaming keyword spotting on mobile devices](https://mlsanity.com/paper/2005.06720/) (2020)
- [A neural attention model for speech command recognition](https://mlsanity.com/paper/1808.08929/) (2018)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1703.05390/) (2017)
- [Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutions](https://mlsanity.com/paper/1911.02086/) (2019)
- [Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1705.02411/) (2017)
- [Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2004.12200/) (2020)
- [Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolution](https://mlsanity.com/paper/2010.09960/) (2020)
- [Neural Architecture Search For Keyword Spotting](https://mlsanity.com/paper/2009.00165/) (2020)
- [AutoKWS: Keyword Spotting with Differentiable Architecture Search](https://mlsanity.com/paper/2009.03658/) (2020)
- [Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigation](https://mlsanity.com/paper/2105.06598/) (2021)
- [Small-footprint Keyword Spotting with Graph Convolutional Network](https://mlsanity.com/paper/1912.05124/) (2019)
- [Wake Word Detection with Alignment-Free Lattice-Free MMI](https://mlsanity.com/paper/2005.08347/) (2020)
- [Efficient keyword spotting using time delay neural networks](https://mlsanity.com/paper/1807.04353/) (2018)

## Built on by (most cited first, 2 of 2)

- [Graph Canvas for Controllable 3D Scene Generation](https://mlsanity.com/paper/2412.00091/) (2024)
- [Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensembles](https://mlsanity.com/paper/2310.11379/) (2023)

## Abstract (arXiv)

Keyword spotting systems continuously process audio streams to detect keywords. One of the most challenging tasks in designing such systems is to reduce False Alarm (FA) which happens when the system falsely registers a keyword despite the keyword not being uttered. In this paper, we propose a simple yet elegant solution to this problem that follows from the law of total probability. We show that existing deep keyword spotting mechanisms can be improved by Successive Refinement, where the system first classifies whether the input audio is speech or not, followed by whether the input is keyword-like or not, and finally classifies which keyword was uttered. We show across multiple models with size ranging from 13K parameters to 2.41M parameters, the successive refinement technique reduces FA by up to a factor of 8 on in-domain held-out FA data, and up to a factor of 7 on out-of-domain (OOD) FA data. Further, our proposed approach is "plug-and-play" and can be applied to any deep keyword spotting model.