  Many speech processing tasks involve measuring the acoustic similarity
between speech segments. Acoustic word embeddings (AWE) allow for efficient
comparisons by mapping speech segments of arbitrary duration to
fixed-dimensional vectors. For zero-resource speech processing, where
unlabelled speech is the only available resource, some of the best AWE
approaches rely on weak top-down constraints in the form of automatically
discovered word-like segments. Rather than learning embeddings at the segment
level, another line of zero-resource research has looked at representation
learning at the short-time frame level. Recent approaches include
self-supervised predictive coding and correspondence autoencoder (CAE) models.
In this paper we consider whether these frame-level features are beneficial
when used as inputs for training to an unsupervised AWE model. We compare
frame-level features from contrastive predictive coding (CPC), autoregressive
predictive coding and a CAE to conventional MFCCs. These are used as inputs to
a recurrent CAE-based AWE model. In a word discrimination task on English and
Xitsonga data, all three representation learning approaches outperform MFCCs,
with CPC consistently showing the biggest improvement. In cross-lingual
experiments we find that CPC features trained on English can also be
transferred to Xitsonga.
