mlsanityData to 7 Oct 2026

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

arXiv 2304.03416 ↗First posted 6 Apr 2023eess.SP cs.LG cs.SDcited by 2

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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.

Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 21 of its 32 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

21 of 32 references.

References · 21

  1. 2018Speech Commands: A Dataset for Limited-Vocabulary Speech Recognitioncited by 741
  2. 2015MUSAN: A Music, Speech, and Noise Corpuscited by 400
  3. 2018Looking to Listen at the Cocktail Party: A Speaker-Independent Audio-Visual Model for Speech Separationcited by 160
  4. 2017Deep Residual Learning for Small-Footprint Keyword Spottingcited by 68
  5. 2021Keyword Transformer: A Self-Attention Model for Keyword Spottingcited by 59
  6. 2019Temporal Convolution for Real-time Keyword Spotting on Mobile Devicescited by 48
  7. 2020Streaming keyword spotting on mobile devicescited by 47
  8. 2021Broadcasted Residual Learning for Efficient Keyword Spottingcited by 47
  9. 2018A neural attention model for speech command recognitioncited by 44
  10. 2021Deep Spoken Keyword Spotting: An Overviewcited by 43
  11. 2017Convolutional Recurrent Neural Networks for Small-Footprint Keyword Spottingcited by 30
  12. 2019Small-Footprint Keyword Spotting on Raw Audio Data with Sinc-Convolutionscited by 20
9 more
  1. 2017Max-Pooling Loss Training of Long Short-Term Memory Networks for Small-Footprint Keyword Spottingcited by 15
  2. 2020Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spottingcited by 15
  3. 2020Small-Footprint Keyword Spotting with Multi-Scale Temporal Convolutioncited by 15
  4. 2020Neural Architecture Search For Keyword Spottingcited by 11
  5. 2020AutoKWS: Keyword Spotting with Differentiable Architecture Searchcited by 10
  6. 2021Streaming Transformer for Hardware Efficient Voice Trigger Detection and False Trigger Mitigationcited by 8
  7. 2019Small-footprint Keyword Spotting with Graph Convolutional Networkcited by 7
  8. 2020Wake Word Detection with Alignment-Free Lattice-Free MMIcited by 6
  9. 2018Efficient keyword spotting using time delay neural networkscited by 5

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Cited by year

Citing papers per year: 2023: 1; 2024: 1.

2 papers.

  1. 2024Graph Canvas for Controllable 3D Scene Generationcited by 11
  2. 2023Robust Wake-Up Word Detection by Two-stage Multi-resolution Ensemblescited by 1