# Encoder-Decoder Neural Architecture Optimization for Keyword Spotting

arXiv: https://arxiv.org/abs/2106.02738 · HTML: https://mlsanity.com/paper/2106.02738/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 25 references that are in the map.
- First posted on arXiv 4 Jun 2021.

## Builds on (most cited first, 22 of 22)

- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Learning Transferable Architectures for Scalable Image Recognition](https://mlsanity.com/paper/1707.07012/) (2017)
- [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://mlsanity.com/paper/1804.03209/) (2018)
- [Progressive Neural Architecture Search](https://mlsanity.com/paper/1712.00559/) (2017)
- [SNAS: Stochastic Neural Architecture Search](https://mlsanity.com/paper/1812.09926/) (2018)
- [Neural Architecture Optimization](https://mlsanity.com/paper/1808.07233/) (2018)
- [Fair DARTS: Eliminating Unfair Advantages in Differentiable Architecture Search](https://mlsanity.com/paper/1911.12126/) (2019)
- [Hello Edge: Keyword Spotting on Microcontrollers](https://mlsanity.com/paper/1711.07128/) (2017)
- [Deep Residual Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/1710.10361/) (2017)
- [Temporal Convolution for Real-time Keyword Spotting on Mobile Devices](https://mlsanity.com/paper/1904.03814/) (2019)
- [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)
- [MatchboxNet: 1D Time-Channel Separable Convolutional Neural Network Architecture for Speech Commands Recognition](https://mlsanity.com/paper/2004.08531/) (2020)
- [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)
- [Noisy Differentiable Architecture Search](https://mlsanity.com/paper/2005.03566/) (2020)
- [Depthwise Separable Convolutional ResNet with Squeeze-and-Excitation Blocks for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2004.12200/) (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)
- [Metric Learning for Keyword Spotting](https://mlsanity.com/paper/2005.08776/) (2020)
- [Stochastic Adaptive Neural Architecture Search for Keyword Spotting](https://mlsanity.com/paper/1811.06753/) (2018)

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

- [Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection](https://mlsanity.com/paper/2501.11631/) (2025)
- [Axial-UNet: A Neural Weather Model for Precipitation Nowcasting](https://mlsanity.com/paper/2504.19408/) (2025)

## Abstract (arXiv)

Keyword spotting aims to identify specific keyword audio utterances. In recent years, deep convolutional neural networks have been widely utilized in keyword spotting systems. However, their model architectures are mainly based on off-the shelfbackbones such as VGG-Net or ResNet, instead of specially designed for the task. In this paper, we utilize neural architecture search to design convolutional neural network models that can boost the performance of keyword spotting while maintaining an acceptable memory footprint. Specifically, we search the model operators and their connections in a specific search space with Encoder-Decoder neural architecture optimization. Extensive evaluations on Google's Speech Commands Dataset show that the model architecture searched by our approach achieves a state-of-the-art accuracy of over 97%.