# Efficient keyword spotting using dilated convolutions and gating

arXiv: https://arxiv.org/abs/1811.07684 · HTML: https://mlsanity.com/paper/1811.07684/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2018.

## Built on by (most cited first, 21 of 21)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Read and Attend: Temporal Localisation in Sign Language Videos](https://mlsanity.com/paper/2103.16481/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Scaling up sign spotting through sign language dictionaries](https://mlsanity.com/paper/2205.04152/) (2022)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [WaveSense: Efficient Temporal Convolutions with Spiking Neural Networks for Keyword Spotting](https://mlsanity.com/paper/2111.01456/) (2021)
- [Supervised training of spiking neural networks for robust deployment on mixed-signal neuromorphic processors](https://mlsanity.com/paper/2102.06408/) (2021)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [Delayed Memory Unit: Modelling Temporal Dependency Through Delay Gate](https://mlsanity.com/paper/2310.14982/) (2023)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [Enhancing Voice Wake-Up for Dysarthria: Mandarin Dysarthria Speech Corpus Release and Customized System Design](https://mlsanity.com/paper/2406.10304/) (2024)
- [Self-Powered LLM Modality Expansion for Large Speech-Text Models](https://mlsanity.com/paper/2410.03798/) (2024)
- [Low-resource Low-footprint Wake-word Detection using Knowledge Distillation](https://mlsanity.com/paper/2207.03331/) (2022)
- [Exploring Representation Learning for Small-Footprint Keyword Spotting](https://mlsanity.com/paper/2303.10912/) (2023)
- [Disentangled Training with Adversarial Examples For Robust Small-footprint Keyword Spotting](https://mlsanity.com/paper/2408.13355/) (2024)
- [Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches](https://mlsanity.com/paper/2302.08950/) (2023)
- [AraSpot: Arabic Spoken Command Spotting](https://mlsanity.com/paper/2303.16621/) (2023)
- [Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data](https://mlsanity.com/paper/2309.00647/) (2023)
- [CALICO: Conversational Agent Localization via Synthetic Data Generation](https://mlsanity.com/paper/2412.05388/) (2024)
- [PatchDSU: Uncertainty Modeling for Out of Distribution Generalization in Keyword Spotting](https://mlsanity.com/paper/2508.03190/) (2025)
- [OnDA: On-device Channel Pruning for Efficient Personalized Keyword Spotting](https://mlsanity.com/paper/2603.02247/) (2026)

## Abstract (arXiv)

We explore the application of end-to-end stateless temporal modeling to small-footprint keyword spotting as opposed to recurrent networks that model long-term temporal dependencies using internal states. We propose a model inspired by the recent success of dilated convolutions in sequence modeling applications, allowing to train deeper architectures in resource-constrained configurations. Gated activations and residual connections are also added, following a similar configuration to WaveNet. In addition, we apply a custom target labeling that back-propagates loss from specific frames of interest, therefore yielding higher accuracy and only requiring to detect the end of the keyword. Our experimental results show that our model outperforms a max-pooling loss trained recurrent neural network using LSTM cells, with a significant decrease in false rejection rate. The underlying dataset - "Hey Snips" utterances recorded by over 2.2K different speakers - has been made publicly available to establish an open reference for wake-word detection.