  Acoustic word embeddings (AWEs) are fixed-dimensional vector representations
of speech segments that encode phonetic content so that different realisations
of the same word have similar embeddings. In this paper we explore semantic AWE
modelling. These AWEs should not only capture phonetics but also the meaning of
a word (similar to textual word embeddings). We consider the scenario where we
only have untranscribed speech in a target language. We introduce a number of
strategies leveraging a pre-trained multilingual AWE model -- a phonetic AWE
model trained on labelled data from multiple languages excluding the target.
Our best semantic AWE approach involves clustering word segments using the
multilingual AWE model, deriving soft pseudo-word labels from the cluster
centroids, and then training a Skipgram-like model on the soft vectors. In an
intrinsic word similarity task measuring semantics, this multilingual transfer
approach outperforms all previous semantic AWE methods. We also show -- for the
first time -- that AWEs can be used for downstream semantic query-by-example
search.
