  Spoken language understanding (SLU) tasks involve mapping from speech audio
signals to semantic labels. Given the complexity of such tasks, good
performance might be expected to require large labeled datasets, which are
difficult to collect for each new task and domain. However, recent advances in
self-supervised speech representations have made it feasible to consider
learning SLU models with limited labeled data. In this work we focus on
low-resource spoken named entity recognition (NER) and address the question:
Beyond self-supervised pre-training, how can we use external speech and/or text
data that are not annotated for the task? We draw on a variety of approaches,
including self-training, knowledge distillation, and transfer learning, and
consider their applicability to both end-to-end models and pipeline (speech
recognition followed by text NER model) approaches. We find that several of
these approaches improve performance in resource-constrained settings beyond
the benefits from pre-trained representations alone. Compared to prior work, we
find improved F1 scores of up to 16%. While the best baseline model is a
pipeline approach, the best performance when using external data is ultimately
achieved by an end-to-end model. We provide detailed comparisons and analyses,
showing for example that end-to-end models are able to focus on the more
NER-specific words.
