  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.
