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In this paper, we study how word-like units are represented and activated ina recurrent neural model of visually grounded speech. The model used in ourexperiments is trained to project an image and its spoken description in acommon representation space. We show that a recurrent model trained on spokensentences implicitly segments its input into word-like units and reliably mapsthem to their correct visual referents. We introduce a methodology originatingfrom linguistics to analyse the representation learned by neural networks --the gating paradigm -- and show that the correct representation of a word isonly 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, butthat some frames have a crucial effect on it. Finally, we suggest that wordrepresentation could be activated through a process of lexical competition.

Word Recognition, Competition, and Activation in a Model of Visually Grounded Speech