# Metric Learning for User-defined Keyword Spotting

arXiv: https://arxiv.org/abs/2211.00439 · HTML: https://mlsanity.com/paper/2211.00439/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2022.

## Built on by (most cited first, 7 of 7)

- [Flexible Keyword Spotting based on Homogeneous Audio-Text Embedding](https://mlsanity.com/paper/2308.06472/) (2023)
- [VoiceDiT: Dual-Condition Diffusion Transformer for Environment-Aware Speech Synthesis](https://mlsanity.com/paper/2412.19259/) (2024)
- [Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting Systems](https://mlsanity.com/paper/2306.02161/) (2023)
- [Open-vocabulary Keyword-spotting with Adaptive Instance Normalization](https://mlsanity.com/paper/2309.08561/) (2023)
- [Self-Learning for Personalized Keyword Spotting on Ultra-Low-Power Audio Sensors](https://mlsanity.com/paper/2408.12481/) (2024)
- [Text-Aware Adapter for Few-Shot Keyword Spotting](https://mlsanity.com/paper/2412.18142/) (2024)
- [PCOV-KWS: Multi-task Learning for Personalized Customizable Open Vocabulary Keyword Spotting](https://mlsanity.com/paper/2603.18023/) (2026)

## Abstract (arXiv)

The goal of this work is to detect new spoken terms defined by users. While most previous works address Keyword Spotting (KWS) as a closed-set classification problem, this limits their transferability to unseen terms. The ability to define custom keywords has advantages in terms of user experience. In this paper, we propose a metric learning-based training strategy for user-defined keyword spotting. In particular, we make the following contributions: (1) we construct a large-scale keyword dataset with an existing speech corpus and propose a filtering method to remove data that degrade model training; (2) we propose a metric learning-based two-stage training strategy, and demonstrate that the proposed method improves the performance on the user-defined keyword spotting task by enriching their representations; (3) to facilitate the fair comparison in the user-defined KWS field, we propose unified evaluation protocol and metrics. Our proposed system does not require an incremental training on the user-defined keywords, and outperforms previous works by a significant margin on the Google Speech Commands dataset using the proposed as well as the existing metrics.