# Attention-based End-to-End Models for Small-Footprint Keyword Spotting

arXiv: https://arxiv.org/abs/1803.10916 · HTML: https://mlsanity.com/paper/1803.10916/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2018.

## Built on by (most cited first, 18 of 18)

- [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)
- [Defending against Adversarial Audio via Diffusion Model](https://mlsanity.com/paper/2303.01507/) (2023)
- [Wake Word Detection with Streaming Transformers](https://mlsanity.com/paper/2102.04488/) (2021)
- [Personalized Keyphrase Detection using Speaker and Environment Information](https://mlsanity.com/paper/2104.13970/) (2021)
- [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)
- [Text Anchor Based Metric Learning for Small-footprint Keyword Spotting](https://mlsanity.com/paper/2108.05516/) (2021)
- [End-to-End Speaker Height and age estimation using Attention Mechanism with LSTM-RNN](https://mlsanity.com/paper/2101.05056/) (2021)
- [Auto-KWS 2021 Challenge: Task, Datasets, and Baselines](https://mlsanity.com/paper/2104.00513/) (2021)
- [Learning Decoupling Features Through Orthogonality Regularization](https://mlsanity.com/paper/2203.16772/) (2022)
- [Speech Augmentation Based Unsupervised Learning for Keyword Spotting](https://mlsanity.com/paper/2205.14329/) (2022)
- [Towards noise robust trigger-word detection with contrastive learning pre-task for fast on-boarding of new trigger-words](https://mlsanity.com/paper/2111.03971/) (2021)
- [AB/BA analysis: A framework for estimating keyword spotting recall improvement while maintaining audio privacy](https://mlsanity.com/paper/2204.08474/) (2022)
- [AraSpot: Arabic Spoken Command Spotting](https://mlsanity.com/paper/2303.16621/) (2023)
- [Query-by-Example Keyword Spotting Using Spectral-Temporal Graph Attentive Pooling and Multi-Task Learning](https://mlsanity.com/paper/2409.00099/) (2024)
- [Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices](https://mlsanity.com/paper/2508.04857/) (2025)
- [PromptKWS: A Novel Prompt-Guided Open-Vocabulary Keyword Spotting Framework](https://mlsanity.com/paper/2608.28640/) (2026)

## Abstract (arXiv)

In this paper, we propose an attention-based end-to-end neural approach for small-footprint keyword spotting (KWS), which aims to simplify the pipelines of building a production-quality KWS system. Our model consists of an encoder and an attention mechanism. The encoder transforms the input signal into a high level representation using RNNs. Then the attention mechanism weights the encoder features and generates a fixed-length vector. Finally, by linear transformation and softmax function, the vector becomes a score used for keyword detection. We also evaluate the performance of different encoder architectures, including LSTM, GRU and CRNN. Experiments on real-world wake-up data show that our approach outperforms the recent Deep KWS approach by a large margin and the best performance is achieved by CRNN. To be more specific, with ~84K parameters, our attention-based model achieves 1.02% false rejection rate (FRR) at 1.0 false alarm (FA) per hour.