  Semantically-aligned $(speech, image)$ datasets can be used to explore
"visually-grounded speech". In a majority of existing investigations, features
of an image signal are extracted using neural networks "pre-trained" on other
tasks (e.g., classification on ImageNet). In still others, pre-trained networks
are used to extract audio features prior to semantic embedding. Without
"transfer learning" through pre-trained initialization or pre-trained feature
extraction, previous results have tended to show low rates of recall in $speech
\rightarrow image$ and $image \rightarrow speech$ queries.
  Choosing appropriate neural architectures for encoders in the speech and
image branches and using large datasets, one can obtain competitive recall
rates without any reliance on any pre-trained initialization or feature
extraction: $(speech,image)$ semantic alignment and $speech \rightarrow image$
and $image \rightarrow speech$ retrieval are canonical tasks worthy of
independent investigation of their own and allow one to explore other
questions---e.g., the size of the audio embedder can be reduced significantly
with little loss of recall rates in $speech \rightarrow image$ and $image
\rightarrow speech$ queries.
