  Spoken intent detection has become a popular approach to interface with
various smart devices with ease. However, such systems are limited to the
preset list of intents-terms or commands, which restricts the quick
customization of personal devices to new intents. This paper presents a
few-shot spoken intent classification approach with task-agnostic
representations via meta-learning paradigm. Specifically, we leverage the
popular representation-based meta-learning learning to build a task-agnostic
representation of utterances, that then use a linear classifier for prediction.
We evaluate three such approaches on our novel experimental protocol developed
on two popular spoken intent classification datasets: Google Commands and the
Fluent Speech Commands dataset. For a 5-shot (1-shot) classification of novel
classes, the proposed framework provides an average classification accuracy of
88.6% (76.3%) on the Google Commands dataset, and 78.5% (64.2%) on the Fluent
Speech Commands dataset. The performance is comparable to traditionally
supervised classification models with abundant training samples.
