# An Anchor-Free Detector for Continuous Speech Keyword Spotting

arXiv: https://arxiv.org/abs/2208.04622 · HTML: https://mlsanity.com/paper/2208.04622/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Aug 2022.

## Built on by (most cited first, 1 of 1)

- [Improving vision-inspired keyword spotting using dynamic module skipping in streaming conformer encoder](https://mlsanity.com/paper/2309.00140/) (2023)

## Abstract (arXiv)

Continuous Speech Keyword Spotting (CSKWS) is a task to detect predefined keywords in a continuous speech. In this paper, we regard CSKWS as a one-dimensional object detection task and propose a novel anchor-free detector, named AF-KWS, to solve the problem. AF-KWS directly regresses the center locations and lengths of the keywords through a single-stage deep neural network. In particular, AF-KWS is tailored for this speech task as we introduce an auxiliary unknown class to exclude other words from non-speech or silent background. We have built two benchmark datasets named LibriTop-20 and continuous meeting analysis keywords (CMAK) dataset for CSKWS. Evaluations on these two datasets show that our proposed AF-KWS outperforms reference schemes by a large margin, and therefore provides a decent baseline for future research.