  Unsupervised spoken term discovery consists of two tasks: finding the
acoustic segment boundaries and labeling acoustically similar segments with the
same labels. We perform segmentation based on the assumption that the frame
feature vectors are more similar within a segment than across the segments.
Therefore, for strong segmentation performance, it is crucial that the features
represent the phonetic properties of a frame more than other factors of
variability. We achieve this via a self-expressing autoencoder framework. It
consists of a single encoder and two decoders with shared weights. The encoder
projects the input features into a latent representation. One of the decoders
tries to reconstruct the input from these latent representations and the other
from the self-expressed version of them. We use the obtained features to
segment and cluster the speech data. We evaluate the performance of the
proposed method in the Zero Resource 2020 challenge unit discovery task. The
proposed system consistently outperforms the baseline, demonstrating the
usefulness of the method in learning representations.
