Computational modelling of human spoken-word recognition: the effects of pre-lexical representation quality on Fine-Tracker’s modelling performance
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2017-10-26
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en
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Abstract
Fine-Tracker is a model of human speech recognition that is able to model
the use of durational cues for the disambiguation of temporarily ambiguous
speech. While previous Fine-Tracker simulations were successful at modelling
human behavioural data on the use of durational cues, Fine-Tracker is not a
very good recogniser of speech. This study proposes to improve the quality
of Fine-Tracker's pre-lexical representations by using deep convolutional neural
networks for extracting the pre-lexical representations from the speech signal.
The convolutional neural networks resulted in large increases in the classifi cation
accuracy of the pre-lexical level features. The improvement in the quality
of the pre-lexical representations resulted in better word recognition for Fine-
Tracker simulations. However, the improved word recognition did not improve
Fine-Tracker's simulations of the use of durational information compared to
simulations reported in previous studies.
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Faculteit der Sociale Wetenschappen