Before revision: We investigate the effect of introducing phone, syllable, or word boundaries on the performance of a Model of Visually Grounded Speech and compare the results with a model that does not use any boundary informationand with a model that uses random boundaries.
After revision: The language acquisition literature shows that children do not build their lexicon by segmenting the spoken input into phonemes and then building up words from them, but rather adopt a top-down approach and start by segmenting word-like units and then break them down into smaller units. This suggests that the ideal way of learning a language is by starting from full semantic units. In this paper, we investigate if this is also the case for a neural model of Visually Grounded Speech and compare the results with a model that does not use any boundary informationand with a model that uses random boundaries.