  Learning to recognize new keywords with just a few examples is essential for
personalizing keyword spotting (KWS) models to a user's choice of keywords.
However, modern KWS models are typically trained on large datasets and
restricted to a small vocabulary of keywords, limiting their transferability to
a broad range of unseen keywords. Towards easily customizable KWS models, we
present KeySEM (Keyword Speech EMbedding), a speech embedding model pre-trained
on the task of recognizing a large number of keywords. Speech representations
offered by KeySEM are highly effective for learning new keywords from a limited
number of examples. Comparisons with a diverse range of related work across
several datasets show that our method achieves consistently superior
performance with fewer training examples. Although KeySEM was pre-trained only
on English utterances, the performance gains also extend to datasets from four
other languages indicating that KeySEM learns useful representations well
aligned with the task of keyword spotting. Finally, we demonstrate KeySEM's
ability to learn new keywords sequentially without requiring to re-train on
previously learned keywords. Our experimental observations suggest that KeySEM
is well suited to on-device environments where post-deployment learning and
ease of customization are often desirable.

Audio and Speech Processing;Computation and Language;Machine Learning;Sound