Improving Small Footprint Few-shot Keyword Spotting with Supervision on
  Auxiliary Data
Seunghan Yang Byeonggeun Kim Kyuhong Shim Simyung Chang
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.