  In zero-resource settings where transcribed speech audio is unavailable,
unsupervised feature learning is essential for downstream speech processing
tasks. Here we compare two recent methods for frame-level acoustic feature
learning. For both methods, unsupervised term discovery is used to find pairs
of word examples of the same unknown type. Dynamic programming is then used to
align the feature frames between each word pair, serving as weak top-down
supervision for the two models. For the correspondence autoencoder (CAE),
matching frames are presented as input-output pairs. The Triamese network uses
a contrastive loss to reduce the distance between frames of the same predicted
word type while increasing the distance between negative examples. For the
first time, these feature extractors are compared on the same discrimination
tasks using the same weak supervision pairs. We find that, on the two datasets
considered here, the CAE outperforms the Triamese network. However, we show
that a new hybrid correspondence-Triamese approach (CTriamese), consistently
outperforms both the CAE and Triamese models in terms of average precision and
ABX error rates on both English and Xitsonga evaluation data.
