# DONUT: CTC-based Query-by-Example Keyword Spotting

arXiv: https://arxiv.org/abs/1811.10736 · HTML: https://mlsanity.com/paper/1811.10736/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2018.

## Built on by (most cited first, 15 of 15)

- [Deep Spoken Keyword Spotting: An Overview](https://mlsanity.com/paper/2111.10592/) (2021)
- [Query-by-Example Keyword Spotting system using Multi-head Attention and Softtriple Loss](https://mlsanity.com/paper/2102.07061/) (2021)
- [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)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [QbyE-MLPMixer: Query-by-Example Open-Vocabulary Keyword Spotting using MLPMixer](https://mlsanity.com/paper/2206.13231/) (2022)
- [Speech Understanding on Tiny Devices with A Learning Cache](https://mlsanity.com/paper/2311.18188/) (2023)
- [Latent Phrase Matching for Dysarthric Speech](https://mlsanity.com/paper/2306.05446/) (2023)
- [End-to-End User-Defined Keyword Spotting using Shifted Delta Coefficients](https://mlsanity.com/paper/2405.14489/) (2024)
- [Improving Small Footprint Few-shot Keyword Spotting with Supervision on Auxiliary Data](https://mlsanity.com/paper/2309.00647/) (2023)
- [CTC-aligned Audio-Text Embedding for Streaming Open-vocabulary Keyword Spotting](https://mlsanity.com/paper/2406.07923/) (2024)
- [SLiCK: Exploiting Subsequences for Length-Constrained Keyword Spotting](https://mlsanity.com/paper/2409.09067/) (2024)
- [Keyword Spotting with Hyper-Matched Filters for Small Footprint Devices](https://mlsanity.com/paper/2508.04857/) (2025)
- [PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting](https://mlsanity.com/paper/2603.18023/) (2026)
- [ProKWS: Personalized Keyword Spotting via Collaborative Learning of Phonemes and Prosody](https://mlsanity.com/paper/2603.18024/) (2026)

## Abstract (arXiv)

Keyword spotting--or wakeword detection--is an essential feature for hands-free operation of modern voice-controlled devices. With such devices becoming ubiquitous, users might want to choose a personalized custom wakeword. In this work, we present DONUT, a CTC-based algorithm for online query-by-example keyword spotting that enables custom wakeword detection. The algorithm works by recording a small number of training examples from the user, generating a set of label sequence hypotheses from these training examples, and detecting the wakeword by aggregating the scores of all the hypotheses given a new audio recording. Our method combines the generalization and interpretability of CTC-based keyword spotting with the user-adaptation and convenience of a conventional query-by-example system. DONUT has low computational requirements and is well-suited for both learning and inference on embedded systems without requiring private user data to be uploaded to the cloud.