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

William N. Havard, Jean-Pierre Chevrot, Laurent Besacier


Abstract
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.
Anthology ID:
K19-1032
Volume:
Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venue:
CoNLL
SIG:
SIGNLL
Publisher:
Association for Computational Linguistics
Note:
Pages:
339–348
Language:
URL:
https://www.aclweb.org/anthology/K19-1032
DOI:
10.18653/v1/K19-1032
Bib Export formats:
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PDF:
http://aclanthology.lst.uni-saarland.de/K19-1032.pdf
Supplementary material:
 K19-1032.Supplementary_Material.pdf