# Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting

arXiv: https://arxiv.org/abs/1710.06554 · HTML: https://mlsanity.com/paper/1710.06554/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2017.

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

- [Can You Hear It? Backdoor Attacks via Ultrasonic Triggers](https://mlsanity.com/paper/2107.14569/) (2021)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [Ultra-Low Power Keyword Spotting at the Edge](https://mlsanity.com/paper/2111.04988/) (2021)
- [Speech Enhancement for Wake-Up-Word detection in Voice Assistants](https://mlsanity.com/paper/2101.12732/) (2021)
- [End-to-end Keyword Spotting using Neural Architecture Search and Quantization](https://mlsanity.com/paper/2104.06666/) (2021)
- [Mobiprox: Supporting Dynamic Approximate Computing on Mobiles](https://mlsanity.com/paper/2303.11291/) (2023)

## Abstract (arXiv)

We describe Honk, an open-source PyTorch reimplementation of convolutional neural networks for keyword spotting that are included as examples in TensorFlow. These models are useful for recognizing "command triggers" in speech-based interfaces (e.g., "Hey Siri"), which serve as explicit cues for audio recordings of utterances that are sent to the cloud for full speech recognition. Evaluation on Google's recently released Speech Commands Dataset shows that our reimplementation is comparable in accuracy and provides a starting point for future work on the keyword spotting task.