Modeling the use of durational information in human spoken-word recognition

Abstract

Evidence that listeners, at least in a laboratory environment, use durational cues to help resolve temporarily ambiguous speech input has accumulated over the past decades. This paper introduces Fine-Tracker, a computational model of word recognition specifically designed for "tracking" fine-phonetic information in the acoustic speech signal and using it during word recognition. Two simulations were carried out using real speech as input to the model. The simulations showed that the Fine-Tracker, as has been found for humans, benefits from durational information during word recognition, and uses it to disambiguate the incoming speech signal. The availability of durational information allows the computational model to distinguish embedded words from their matrix words (first simulation), and to distinguish word final realizations of [s] from word initial realizations (second simulation). Fine-Tracker thus provides the first computational model of human word recognition that is able to extract durational information from the speech signal and to use it to differentiate words.