  Recent studies have introduced methods for learning acoustic word embeddings
(AWEs)---fixed-size vector representations of words which encode their acoustic
features. Despite the widespread use of AWEs in speech processing research,
they have only been evaluated quantitatively in their ability to discriminate
between whole word tokens. To better understand the applications of AWEs in
various downstream tasks and in cognitive modeling, we need to analyze the
representation spaces of AWEs. Here we analyze basic properties of AWE spaces
learned by a sequence-to-sequence encoder-decoder model in six typologically
diverse languages. We first show that these AWEs preserve some information
about words' absolute duration and speaker. At the same time, the
representation space of these AWEs is organized such that the distance between
words' embeddings increases with those words' phonetic dissimilarity. Finally,
the AWEs exhibit a word onset bias, similar to patterns reported in various
studies on human speech processing and lexical access. We argue this is a
promising result and encourage further evaluation of AWEs as a potentially
useful tool in cognitive science, which could provide a link between speech
processing and lexical memory.
