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Abstract:
This paper describes a computational model of speech comprehension that takes the acoustic signal as input and predicts
reaction times as observed in an auditory lexical decision task. By doing so, we explore a new generation of end-to-end computational models that are able to simulate the behaviour of human subjects participating in a psycholinguistic experiment. So far, nearly all computational models of speech comprehension do not start from the speech signal itself, but from abstract representations of the speech signal, while the few existing models that do start from the acoustic signal cannot directly model reaction times as obtained in comprehension experiments. The main
functional components in our model are the perception stage, which is compatible with the psycholinguistic model Shortlist B and is implemented with techniques from automatic speech recognition, and the decision stage, which is based on the linear
ballistic accumulation decision model. We successfully tested our model against data from 20 participants performing a largescale
auditory lexical decision experiment. Analyses show that the model is a good predictor for the average judgment and reaction time for each word.
