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