  For languages with insufficient resources to train speech recognition
systems, query-by-example spoken term detection (QbE-STD) offers a way of
accessing an untranscribed speech corpus by helping identify regions where
spoken query terms occur. Yet retrieval performance can be poor when the query
and corpus are spoken by different speakers and produced in different recording
conditions. Using data selected from a variety of speakers and recording
conditions from 7 Australian Aboriginal languages and a regional variety of
Dutch, all of which are endangered or vulnerable, we evaluated whether QbE-STD
performance on these languages could be improved by leveraging representations
extracted from the pre-trained English wav2vec 2.0 model. Compared to the use
of Mel-frequency cepstral coefficients and bottleneck features, we find that
representations from the middle layers of the wav2vec 2.0 Transformer offer
large gains in task performance (between 56% and 86%). While features extracted
using the pre-trained English model yielded improved detection on all the
evaluation languages, better detection performance was associated with the
evaluation language's phonological similarity to English.
