# Query-by-example on-device keyword spotting

arXiv: https://arxiv.org/abs/1910.05171 · HTML: https://mlsanity.com/paper/1910.05171/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Oct 2019.

## Built on by (most cited first, 19 of 19)

- [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)
- [Defending against Adversarial Audio via Diffusion Model](https://mlsanity.com/paper/2303.01507/) (2023)
- [Learning Audio-Text Agreement for Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2206.15400/) (2022)
- [Teaching keyword spotters to spot new keywords with limited examples](https://mlsanity.com/paper/2106.02443/) (2021)
- [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)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [MM-KWS: Multi-modal Prompts for Multilingual User-defined Keyword Spotting](https://mlsanity.com/paper/2406.07310/) (2024)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [Prototype-based Personalized Pruning](https://mlsanity.com/paper/2103.15564/) (2021)
- [Improving Voice Trigger Detection with Metric Learning](https://mlsanity.com/paper/2204.02455/) (2022)
- [End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients](https://mlsanity.com/paper/2405.14489/) (2024)
- [Deep Learning-Based Acoustic Mosquito Detection in Noisy Conditions Using Trainable Kernels and Augmentations](https://mlsanity.com/paper/2207.13843/) (2022)
- [Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data](https://mlsanity.com/paper/2309.00647/) (2023)
- [SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting](https://mlsanity.com/paper/2409.09067/) (2024)
- [Noise-Agnostic Multitask Whisper Training for Reducing False Alarm Errors in Call-for-Help Detection](https://mlsanity.com/paper/2501.11631/) (2025)
- [OnDA: On-device Channel Pruning for Efficient Personalized Keyword Spotting](https://mlsanity.com/paper/2603.02247/) (2026)
- [What changes after deployment? A survey on On-device Learning in TinyML](https://mlsanity.com/paper/2605.31226/) (2026)

## Abstract (arXiv)

A keyword spotting (KWS) system determines the existence of, usually predefined, keyword in a continuous speech stream. This paper presents a query-by-example on-device KWS system which is user-specific. The proposed system consists of two main steps: query enrollment and testing. In query enrollment step, phonetic posteriors are output by a small-footprint automatic speech recognition model based on connectionist temporal classification. Using the phonetic-level posteriorgram, hypothesis graph of finite-state transducer (FST) is built, thus can enroll any keywords thus avoiding an out-of-vocabulary problem. In testing, a log-likelihood is scored for input audio using the FST. We propose a threshold prediction method while using the user-specific keyword hypothesis only. The system generates query-specific negatives by rearranging each query utterance in waveform. The threshold is decided based on the enrollment queries and generated negatives. We tested two keywords in English, and the proposed work shows promising performance while preserving simplicity.