# Deep Residual Learning for Small-Footprint Keyword Spotting

arXiv: https://arxiv.org/abs/1710.10361 · HTML: https://mlsanity.com/paper/1710.10361/

## Key facts

- Cited by 68 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2017.

## Built on by (most cited first, 30 of 68)

- [Broadcasted Residual Learning for Efficient Keyword Spotting](https://mlsanity.com/paper/2106.04140/) (2021)
- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Few-Shot Keyword Spotting in Any Language](https://mlsanity.com/paper/2104.01454/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [Learning Efficient Representations for Keyword Spotting with Triplet Loss](https://mlsanity.com/paper/2101.04792/) (2021)
- [Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again](https://mlsanity.com/paper/2210.04427/) (2022)
- [PQK: Model Compression via Pruning, Quantization, and Knowledge Distillation](https://mlsanity.com/paper/2106.14681/) (2021)
- [Differentiable Network Pruning for Microcontrollers](https://mlsanity.com/paper/2110.08350/) (2021)
- [Two Sparsities Are Better Than One: Unlocking the Performance Benefits of Sparse-Sparse Networks](https://mlsanity.com/paper/2112.13896/) (2021)
- [Matching Latent Encoding for Audio-Text based Keyword Spotting](https://mlsanity.com/paper/2306.05245/) (2023)
- [XRBench: An Extended Reality (XR) Machine Learning Benchmark Suite for the Metaverse](https://mlsanity.com/paper/2211.08675/) (2022)
- [The taste of IPA: Towards open-vocabulary keyword spotting and forced alignment in any language](https://mlsanity.com/paper/2311.08323/) (2023)
- [SubSpectral Normalization for Neural Audio Data Processing](https://mlsanity.com/paper/2103.13620/) (2021)
- [Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding](https://mlsanity.com/paper/2308.06472/) (2023)
- [Online Continual Learning in Keyword Spotting for Low-Resource Devices via Pooling High-Order Temporal Statistics](https://mlsanity.com/paper/2307.12660/) (2023)
- [Progressive Continual Learning for Spoken Keyword Spotting](https://mlsanity.com/paper/2201.12546/) (2022)
- [Rainbow Keywords: Efficient Incremental Learning for Online Spoken Keyword Spotting](https://mlsanity.com/paper/2203.16361/) (2022)
- [DREAM: A Dynamic Scheduler for Dynamic Real-time Multi-model ML Workloads](https://mlsanity.com/paper/2212.03414/) (2022)
- [MoCA: Memory-Centric, Adaptive Execution for Multi-Tenant Deep Neural Networks](https://mlsanity.com/paper/2305.05843/) (2023)
- [Dummy Prototypical Networks for Few-Shot Open-Set Keyword Spotting](https://mlsanity.com/paper/2206.13691/) (2022)
- [Personalized Keyword Spotting through Multi-task Learning](https://mlsanity.com/paper/2206.13708/) (2022)
- [Ultra-Low Power Keyword Spotting at the Edge](https://mlsanity.com/paper/2111.04988/) (2021)
- [TeMPO: Efficient Time-Multiplexed Dynamic Photonic Tensor Core for Edge AI with Compact Slow-Light Electro-Optic Modulator](https://mlsanity.com/paper/2402.07393/) (2024)
- [PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection](https://mlsanity.com/paper/2309.06960/) (2023)
- [Avoid Overfitting User Specific Information in Federated Keyword Spotting](https://mlsanity.com/paper/2206.08864/) (2022)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [AnalyticKWS: Towards Exemplar-Free Analytic Class Incremental Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2505.11817/) (2025)
- [Text Anchor Based Metric Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2108.05516/) (2021)
- [EfficientWord-Net: An Open Source Hotword Detection Engine based on One-shot Learning](https://mlsanity.com/paper/2111.00379/) (2021)
- [Streaming Intended Query Detection using E2E Modeling for Continued Conversation](https://mlsanity.com/paper/2208.13322/) (2022)

## Abstract (arXiv)

We explore the application of deep residual learning and dilated convolutions to the keyword spotting task, using the recently-released Google Speech Commands Dataset as our benchmark. Our best residual network (ResNet) implementation significantly outperforms Google's previous convolutional neural networks in terms of accuracy. By varying model depth and width, we can achieve compact models that also outperform previous small-footprint variants. To our knowledge, we are the first to examine these approaches for keyword spotting, and our results establish an open-source state-of-the-art reference to support the development of future speech-based interfaces.