  Acoustic word embeddings (AWEs) are fixed-dimensional representations of
variable-length speech segments. For zero-resource languages where labelled
data is not available, one AWE approach is to use unsupervised
autoencoder-based recurrent models. Another recent approach is to use
multilingual transfer: a supervised AWE model is trained on several
well-resourced languages and then applied to an unseen zero-resource language.
We consider how a recent contrastive learning loss can be used in both the
purely unsupervised and multilingual transfer settings. Firstly, we show that
terms from an unsupervised term discovery system can be used for contrastive
self-supervision, resulting in improvements over previous unsupervised
monolingual AWE models. Secondly, we consider how multilingual AWE models can
be adapted to a specific zero-resource language using discovered terms. We find
that self-supervised contrastive adaptation outperforms adapted multilingual
correspondence autoencoder and Siamese AWE models, giving the best overall
results in a word discrimination task on six zero-resource languages.
