  To segment a signal into blocks to be analyzed, few-shot keyword spotting
(KWS) systems often utilize a sliding window of fixed size. Because of the
varying lengths of different keywords or their spoken instances, choosing the
right window size is a problem: A window should be long enough to contain all
necessary information needed to recognize a keyword but a longer window may
contain irrelevant information such as multiple words or noise and thus makes
it difficult to reliably detect on- and offsets of keywords. We propose TACos,
a novel angular margin loss for deriving two-dimensional embeddings that retain
temporal properties of the underlying speech signal. In experiments conducted
on KWS-DailyTalk, a few-shot KWS dataset presented in this work, using these
embeddings as templates for dynamic time warping is shown to outperform using
other representations or a sliding window and that using time-reversed segments
of the keywords during training improves the performance.
