Computational modelling of human spoken-word recognition: the effects of pre-lexical representation quality on Fine-Tracker’s modelling performance

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dc.contributor.advisorScharenborg, O.E.
dc.contributor.authorMerkx, D.G.M.
dc.date.issued2017-10-26
dc.description.abstractFine-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.en_US
dc.identifier.urihttp://theses.ubn.ru.nl/handle/123456789/5259
dc.language.isoenen_US
dc.thesis.facultyFaculteit der Sociale Wetenschappenen_US
dc.thesis.specialisationMaster Artificial Intelligenceen_US
dc.thesis.studyprogrammeArtificial Intelligenceen_US
dc.thesis.typeMasteren_US
dc.titleComputational modelling of human spoken-word recognition: the effects of pre-lexical representation quality on Fine-Tracker’s modelling performanceen_US
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