# Few-Shot Keyword Spotting With Prototypical Networks

arXiv: https://arxiv.org/abs/2007.14463 · HTML: https://mlsanity.com/paper/2007.14463/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Jul 2020.

## Built on by (most cited first, 18 of 18)

- [Learning from Few Examples: A Summary of Approaches to Few-Shot Learning](https://mlsanity.com/paper/2203.04291/) (2022)
- [Meta Learning for Natural Language Processing: A Survey](https://mlsanity.com/paper/2205.01500/) (2022)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems](https://mlsanity.com/paper/2306.02161/) (2023)
- [Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting](https://mlsanity.com/paper/2206.13691/) (2022)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [On the Efficiency of Integrating Self-supervised Learning and Meta-learning for User-defined Few-shot Keyword Spotting](https://mlsanity.com/paper/2204.00352/) (2022)
- [Auto-KWS 2021 Challenge: Task, Datasets, and Baselines](https://mlsanity.com/paper/2104.00513/) (2021)
- [Federated Few-shot Learning for Cough Classification with Edge Devices](https://mlsanity.com/paper/2309.01076/) (2023)
- [Enhancing Few-shot Keyword Spotting Performance through Pre-Trained Self-supervised Speech Models](https://mlsanity.com/paper/2506.17686/) (2025)
- [Privacy Enhancement for Cloud-Based Few-Shot Learning](https://mlsanity.com/paper/2205.07864/) (2022)
- [Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data](https://mlsanity.com/paper/2309.00647/) (2023)
- [Multitaper mel-spectrograms for keyword spotting](https://mlsanity.com/paper/2407.04662/) (2024)
- [Visually Grounded Speech Models for Low-resource Languages and Cognitive Modelling](https://mlsanity.com/paper/2409.02865/) (2024)
- [OnDA: On-device Channel Pruning for Efficient Personalized Keyword Spotting](https://mlsanity.com/paper/2603.02247/) (2026)
- [Scaling few-shot spoken word classification with generative meta-continual learning](https://mlsanity.com/paper/2605.13075/) (2026)
- [Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations](https://mlsanity.com/paper/2609.27037/) (2026)
- [Towards Model as a Library: Offline, Community-Sourced AI for Low-Resource African Languages](https://mlsanity.com/paper/2609.38574/) (2026)

## Abstract (arXiv)

Recognizing a particular command or a keyword, keyword spotting has been widely used in many voice interfaces such as Amazon's Alexa and Google Home. In order to recognize a set of keywords, most of the recent deep learning based approaches use a neural network trained with a large number of samples to identify certain pre-defined keywords. This restricts the system from recognizing new, user-defined keywords. Therefore, we first formulate this problem as a few-shot keyword spotting and approach it using metric learning. To enable this research, we also synthesize and publish a Few-shot Google Speech Commands dataset. We then propose a solution to the few-shot keyword spotting problem using temporal and dilated convolutions on prototypical networks. Our comparative experimental results demonstrate keyword spotting of new keywords using just a small number of samples.