  Acoustic word embeddings (AWEs) are vector representations such that
different acoustic exemplars of the same word are projected nearby in the
embedding space. In addition to their use in speech technology applications
such as spoken term discovery and keyword spotting, AWE models have been
adopted as models of spoken-word processing in several cognitively motivated
studies and have been shown to exhibit human-like performance in some auditory
processing tasks. Nevertheless, the representational geometry of AWEs remains
an under-explored topic that has not been studied in the literature. In this
paper, we take a closer analytical look at AWEs learned from English speech and
study how the choice of the learning objective and the architecture shapes
their representational profile. To this end, we employ a set of analytic
techniques from machine learning and neuroscience in three different analyses:
embedding space uniformity, word discriminability, and representational
consistency. Our main findings highlight the prominent role of the learning
objective on shaping the representation profile compared to the model
architecture.
