  We compare features for dynamic time warping (DTW) when used to bootstrap
keyword spotting (KWS) in an almost zero-resource setting. Such
quickly-deployable systems aim to support United Nations (UN) humanitarian
relief efforts in parts of Africa with severely under-resourced languages. Our
objective is to identify acoustic features that provide acceptable KWS
performance in such environments. As supervised resource, we restrict ourselves
to a small, easily acquired and independently compiled set of isolated
keywords. For feature extraction, a multilingual bottleneck feature (BNF)
extractor, trained on well-resourced out-of-domain languages, is integrated
with a correspondence autoencoder (CAE) trained on extremely sparse in-domain
data. On their own, BNFs and CAE features are shown to achieve a more than 2%
absolute performance improvement over baseline MFCCs. However, by using BNFs as
input to the CAE, even better performance is achieved, with a more than 11%
absolute improvement in ROC AUC over MFCCs and more than twice as many top-10
retrievals for two evaluated languages, English and Luganda. We conclude that
integrating BNFs with the CAE allows both large out-of-domain and sparse
in-domain resources to be exploited for improved ASR-free keyword spotting.
