Few-shot keyword spotting (FS-KWS) models usually require large-scale
annotated datasets to generalize to unseen target keywords. However, existing
KWS datasets are limited in scale and gathering keyword-like labeled data is
costly undertaking. To mitigate this issue, we propose a framework that uses
easily collectible, unlabeled reading speech data as an auxiliary source.
Self-supervised learning has been widely adopted for learning representations
from unlabeled data; however, it is known to be suitable for large models with
enough capacity and is not practical for training a small footprint FS-KWS
model. Instead, we automatically annotate and filter the data to construct a
keyword-like dataset, LibriWord, enabling supervision on auxiliary data. We
then adopt multi-task learning that helps the model to enhance the
representation power from out-of-domain auxiliary data. Our method notably
improves the performance over competitive methods in the FS-KWS benchmark.

使用未标记的朗读语音数据作为辅助来源，我们提出了一个框架，通过自动注释和过滤数据来构建类似于关键词的数据集，利用多任务学习来提高模型的表示能力，从而显著提高了少样本关键词检测模型性能。