Honk: A PyTorch Reimplementation of Convolutional Neural Networks for Keyword Spotting
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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.
Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Oct 2017.
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Citing papers per year: 2021: 5; 2022: 0; 2023: 1.
6 papers.
- 2021Can You Hear It? Backdoor Attacks via Ultrasonic Triggerscited by 41
- 2021Learning Efficient Representations for Keyword Spotting with Triplet Losscited by 24
- 2021Ultra-Low Power Keyword Spotting at the Edgecited by 6
- 2021Speech Enhancement for Wake-Up-Word detection in Voice Assistantscited by 2
- 2021End-to-end Keyword Spotting using Neural Architecture Search and Quantizationcited by 2
- 2023Mobiprox: Supporting Dynamic Approximate Computing on Mobilescited by 0
- 2023Mobiprox: Supporting Dynamic Approximate Computing on Mobiles16 Mar 2023
- 2021Ultra-Low Power Keyword Spotting at the Edge9 Nov 2021
- 2021Can You Hear It? Backdoor Attacks via Ultrasonic Triggers30 Jul 2021
- 2021End-to-end Keyword Spotting using Neural Architecture Search and Quantization14 Apr 2021
- 2021Speech Enhancement for Wake-Up-Word detection in Voice Assistants29 Jan 2021
- 2021Learning Efficient Representations for Keyword Spotting with Triplet Loss12 Jan 2021