  Query-by-example (QbE) speech search is the task of matching spoken queries
to utterances within a search collection. In low- or zero-resource settings,
QbE search is often addressed with approaches based on dynamic time warping
(DTW). Recent work has found that methods based on acoustic word embeddings
(AWEs) can improve both performance and search speed. However, prior work on
AWE-based QbE has primarily focused on English data and with single-word
queries. In this work, we generalize AWE training to spans of words, producing
acoustic span embeddings (ASE), and explore the application of ASE to QbE with
arbitrary-length queries in multiple unseen languages. We consider the commonly
used setting where we have access to labeled data in other languages (in our
case, several low-resource languages) distinct from the unseen test languages.
We evaluate our approach on the QUESST 2015 QbE tasks, finding that
multilingual ASE-based search is much faster than DTW-based search and
outperforms the best previously published results on this task.
