  Keyword spotting is the task of detecting a keyword in streaming audio.
Conventional keyword spotting targets predefined keywords classification, but
there is growing attention in few-shot (query-by-example) keyword spotting,
e.g., N-way classification given M-shot support samples. Moreover, in
real-world scenarios, there can be utterances from unexpected categories
(open-set) which need to be rejected rather than classified as one of the N
classes. Combining the two needs, we tackle few-shot open-set keyword spotting
with a new benchmark setting, named splitGSC. We propose episode-known dummy
prototypes based on metric learning to detect an open-set better and introduce
a simple and powerful approach, Dummy Prototypical Networks (D-ProtoNets). Our
D-ProtoNets shows clear margins compared to recent few-shot open-set
recognition (FSOSR) approaches in the suggested splitGSC. We also verify our
method on a standard benchmark, miniImageNet, and D-ProtoNets shows the
state-of-the-art open-set detection rate in FSOSR.

Sound;Machine Learning;Audio and Speech Processing