# End-to-End Streaming Keyword Spotting

arXiv: https://arxiv.org/abs/1812.02802 · HTML: https://mlsanity.com/paper/1812.02802/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Dec 2018.

## Built on by (most cited first, 25 of 25)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Noisy student-teacher training for robust keyword spotting](https://mlsanity.com/paper/2106.01604/) (2021)
- [Production federated keyword spotting via distillation, filtering, and joint federated-centralized training](https://mlsanity.com/paper/2204.06322/) (2022)
- [Personalized Keyphrase Detection using Speaker and Environment Information](https://mlsanity.com/paper/2104.13970/) (2021)
- [Tiny, always-on and fragile: Bias propagation through design choices in on-device machine learning workflows](https://mlsanity.com/paper/2201.07677/) (2022)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [End-to-End Open Vocabulary Keyword Search](https://mlsanity.com/paper/2108.10357/) (2021)
- [PhantomSound: Black-Box, Query-Efficient Audio Adversarial Attack via Split-Second Phoneme Injection](https://mlsanity.com/paper/2309.06960/) (2023)
- [LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting](https://mlsanity.com/paper/2211.04635/) (2022)
- [Sharing Low Rank Conformer Weights for Tiny Always-On Ambient Speech Recognition Models](https://mlsanity.com/paper/2303.08343/) (2023)
- [Utilizing TTS Synthesized Data for Efficient Development of Keyword Spotting Model](https://mlsanity.com/paper/2407.18879/) (2024)
- [HEiMDaL: Highly Efficient Method for Detection and Localization of wake-words](https://mlsanity.com/paper/2210.15425/) (2022)
- [Locale Encoding For Scalable Multilingual Keyword Spotting Models](https://mlsanity.com/paper/2302.12961/) (2023)
- [Exploring Sequence-to-Sequence Transformer-Transducer Models for Keyword Spotting](https://mlsanity.com/paper/2211.06478/) (2022)
- [Synth4Kws: Synthesized Speech for User Defined Keyword Spotting in Low Resource Environments](https://mlsanity.com/paper/2407.16840/) (2024)
- [GE2E-KWS: Generalized End-to-End Training and Evaluation for Zero-shot Keyword Spotting](https://mlsanity.com/paper/2410.16647/) (2024)
- [Multi-task Learning with Cross Attention for Keyword Spotting](https://mlsanity.com/paper/2107.07634/) (2021)
- [I see what you hear: a vision-inspired method to localize words](https://mlsanity.com/paper/2210.13567/) (2022)
- [Personalizing Keyword Spotting with Speaker Information](https://mlsanity.com/paper/2311.03419/) (2023)
- [Adversarial training of Keyword Spotting to Minimize TTS Data Overfitting](https://mlsanity.com/paper/2408.10463/) (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)
- [High-precision Voice Search Query Correction via Retrievable Speech-text Embedings](https://mlsanity.com/paper/2401.04235/) (2024)
- [RepCNN: Micro-sized, Mighty Models for Wakeword Detection](https://mlsanity.com/paper/2406.02652/) (2024)
- [GraphemeAug: A Systematic Approach to Synthesized Hard Negative Keyword Spotting Examples](https://mlsanity.com/paper/2505.14814/) (2025)
- [Training Intelligent Voice Assistant Wakeup with Controllable Synthetic Conversations](https://mlsanity.com/paper/2609.27037/) (2026)

## Abstract (arXiv)

We present a system for keyword spotting that, except for a frontend component for feature generation, it is entirely contained in a deep neural network (DNN) model trained "end-to-end" to predict the presence of the keyword in a stream of audio. The main contributions of this work are, first, an efficient memoized neural network topology that aims at making better use of the parameters and associated computations in the DNN by holding a memory of previous activations distributed over the depth of the DNN. The second contribution is a method to train the DNN, end-to-end, to produce the keyword spotting score. This system significantly outperforms previous approaches both in terms of quality of detection as well as size and computation.