  Given the strong results of self-supervised models on various tasks, there
have been surprisingly few studies exploring self-supervised representations
for acoustic word embeddings (AWE), fixed-dimensional vectors representing
variable-length spoken word segments. In this work, we study several
pre-trained models and pooling methods for constructing AWEs with
self-supervised representations. Owing to the contextualized nature of
self-supervised representations, we hypothesize that simple pooling methods,
such as averaging, might already be useful for constructing AWEs. When
evaluating on a standard word discrimination task, we find that HuBERT
representations with mean-pooling rival the state of the art on English AWEs.
More surprisingly, despite being trained only on English, HuBERT
representations evaluated on Xitsonga, Mandarin, and French consistently
outperform the multilingual model XLSR-53 (as well as Wav2Vec 2.0 trained on
English).
