In this paper, we propose a novel end-to-end user-defined keyword spotting
method that utilizes linguistically corresponding patterns between speech and
text sequences. Unlike previous approaches requiring speech keyword enrollment,
our method compares input queries with an enrolled text keyword sequence. To
place the audio and text representations within a common latent space, we adopt
an attention-based cross-modal matching approach that is trained in an
end-to-end manner with monotonic matching loss and keyword classification loss.
We also utilize a de-noising loss for the acoustic embedding network to improve
robustness in noisy environments. Additionally, we introduce the LibriPhrase
dataset, a new short-phrase dataset based on LibriSpeech for efficiently
training keyword spotting models. Our proposed method achieves competitive
results on various evaluation sets compared to other single-modal and
cross-modal baselines.

本文提出一种新颖的端到端的用户定义关键词检测方法，利用语音和文本序列之间的语言对应模式，实现音频和文本表征在共同的隐含空间中；采用基于注意力的跨模态匹配方法，训练具有单调匹配损失和关键词分类损失的模型，并利用去噪损失改善模型在嘈杂环境中的稳健性，同时引入 LibriPhrase 数据集用于高效地训练关键词检测模型；与其他单模态和跨模态基线相比，在各种评估集上取得了有竞争力的结果。