  In this paper, we investigate the manner in which interpretable sub-word
speech units emerge within a convolutional neural network model trained to
associate raw speech waveforms with semantically related natural image scenes.
We show how diphone boundaries can be superficially extracted from the
activation patterns of intermediate layers of the model, suggesting that the
model may be leveraging these events for the purpose of word recognition. We
present a series of experiments investigating the information encoded by these
events.

Computation and Language;Machine Learning;Sound;Audio and Speech Processing