  Background: Computational models of speech recognition often assume that the
set of target words is already given. This implies that these models do not
learn to recognise speech from scratch without prior knowledge and explicit
supervision. Visually grounded speech models learn to recognise speech without
prior knowledge by exploiting statistical dependencies between spoken and
visual input. While it has previously been shown that visually grounded speech
models learn to recognise the presence of words in the input, we explicitly
investigate such a model as a model of human speech recognition.
  Methods: We investigate the time-course of word recognition as simulated by
the model using a gating paradigm to test whether its recognition is affected
by well-known word-competition effects in human speech processing. We
furthermore investigate whether vector quantisation, a technique for discrete
representation learning, aids the model in the discovery and recognition of
words.
  Results/Conclusion: Our experiments show that the model is able to recognise
nouns in isolation and even learns to properly differentiate between plural and
singular nouns. We also find that recognition is influenced by word competition
from the word-initial cohort and neighbourhood density, mirroring word
competition effects in human speech comprehension. Lastly, we find no evidence
that vector quantisation is helpful in discovering and recognising words. Our
gating experiments even show that the vector quantised model requires more of
the input sequence for correct recognition.
