  In this paper, we study how word-like units are represented and activated in
a recurrent neural model of visually grounded speech. The model used in our
experiments is trained to project an image and its spoken description in a
common representation space. We show that a recurrent model trained on spoken
sentences implicitly segments its input into word-like units and reliably maps
them to their correct visual referents. We introduce a methodology originating
from linguistics to analyse the representation learned by neural networks --
the gating paradigm -- and show that the correct representation of a word is
only activated if the network has access to first phoneme of the target word,
suggesting that the network does not rely on a global acoustic pattern.
Furthermore, we find out that not all speech frames (MFCC vectors in our case)
play an equal role in the final encoded representation of a given word, but
that some frames have a crucial effect on it. Finally, we suggest that word
representation could be activated through a process of lexical competition.
