# Howl: A Deployed, Open-Source Wake Word Detection System

arXiv: https://arxiv.org/abs/2008.09606 · HTML: https://mlsanity.com/paper/2008.09606/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Aug 2020.

## Built on by (most cited first, 6 of 6)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer](https://mlsanity.com/paper/2206.13231/) (2022)
- [EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning](https://mlsanity.com/paper/2111.00379/) (2021)
- [Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations](https://mlsanity.com/paper/2609.27037/) (2026)

## Abstract (arXiv)

We describe Howl, an open-source wake word detection toolkit with native support for open speech datasets, like Mozilla Common Voice and Google Speech Commands. We report benchmark results on Speech Commands and our own freely available wake word detection dataset, built from MCV. We operationalize our system for Firefox Voice, a plugin enabling speech interactivity for the Firefox web browser. Howl represents, to the best of our knowledge, the first fully productionized yet open-source wake word detection toolkit with a web browser deployment target. Our codebase is at https://github.com/castorini/howl.