# AutoKWS: Keyword Spotting with Differentiable Architecture Search

arXiv: https://arxiv.org/abs/2009.03658 · HTML: https://mlsanity.com/paper/2009.03658/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 10 of 10)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Personalized Keyword Spotting through Multi-task Learning](https://mlsanity.com/paper/2206.13708/) (2022)
- [End-to-end Keyword Spotting using Neural Architecture Search and Quantization](https://mlsanity.com/paper/2104.06666/) (2021)
- [Encoder-Decoder Neural Architecture Optimization for Keyword Spotting](https://mlsanity.com/paper/2106.02738/) (2021)
- [To Wake-up or Not to Wake-up: Reducing Keyword False Alarm by Successive Refinement](https://mlsanity.com/paper/2304.03416/) (2023)
- [Combining Neural Architecture Search and Automatic Code Optimization: A Survey](https://mlsanity.com/paper/2408.04116/) (2024)
- [A Study of Designing Compact Audio-Visual Wake Word Spotting System Based on Iterative Fine-Tuning in Neural Network Pruning](https://mlsanity.com/paper/2202.08509/) (2022)
- [Energy-Efficient Seizure Detection Suitable for low-power Applications](https://mlsanity.com/paper/2406.16948/) (2024)
- [Keyword Mamba: Spoken Keyword Spotting with State Space Models](https://mlsanity.com/paper/2508.07363/) (2025)
- [PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting](https://mlsanity.com/paper/2603.18023/) (2026)

## Abstract (arXiv)

Smart audio devices are gated by an always-on lightweight keyword spotting program to reduce power consumption. It is however challenging to design models that have both high accuracy and low latency for accurate and fast responsiveness. Many efforts have been made to develop end-to-end neural networks, in which depthwise separable convolutions, temporal convolutions, and LSTMs are adopted as building units. Nonetheless, these networks designed with human expertise may not achieve an optimal trade-off in an expansive search space. In this paper, we propose to leverage recent advances in differentiable neural architecture search to discover more efficient networks. Our searched model attains 97.2% top-1 accuracy on Google Speech Command Dataset v1 with only nearly 100K parameters.