  We present a visually grounded model of speech perception which projects
spoken utterances and images to a joint semantic space. We use a multi-layer
recurrent highway network to model the temporal nature of spoken speech, and
show that it learns to extract both form and meaning-based linguistic knowledge
from the input signal. We carry out an in-depth analysis of the representations
used by different components of the trained model and show that encoding of
semantic aspects tends to become richer as we go up the hierarchy of layers,
whereas encoding of form-related aspects of the language input tends to
initially increase and then plateau or decrease.
