An Anchor-Free Detector for Continuous Speech Keyword Spotting
Abstract
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.
Keywords
Cite
@article{arxiv.2208.04622,
title = {An Anchor-Free Detector for Continuous Speech Keyword Spotting},
author = {Zhiyuan Zhao and Chuanxin Tang and Chengdong Yao and Chong Luo},
journal= {arXiv preprint arXiv:2208.04622},
year = {2022}
}
Comments
Accepted by Interspeech 2022