This thesis investigated lexical representations of children who stutter (CWS) and children who do not stutter (CWNS) using a duration-blocked gating task. This thesis tested the hypothesis that children who stutter have underspecified phonological representations for words, are less sensitive to incremental and segmental information for lexical items, and therefore require more acoustic-phonetic information to activate words in their lexicon. Pilot data collected from fourteen children (ages 5;6 to 10;1): 7 CWS and 7 CWNS matched on age were included in this thesis. Results showed that children in both talker groups required relatively equal amounts of acoustic-phonetic information to identify target words. A regression model revealed that age in months predicted performance on the gating task for CWNS, but that age in months did not predict performance on the gating task for CWS suggesting a difference in the developmental maturity of lexical representations in CWS. Possible conclusions from these pilot data are presented along with recommendations for future research. / text

<div>Cochlear implants (CI) are sensory neuroprostheses capable of partially restoring hearing loss by electrically stimulating the auditory nerve to mimic normal hearing conditions. Despite their success and ongoing advances in both hardware and software, CI patients can still struggle to understand speech, most notably in complex auditory settings, also referred to as the cocktail party problem. Efforts to develop new CI algorithms to overcome this challenge rely on CI simulators and vocoders to test with normal hearing (NH) patients. However, recent studies have suggested that these tools fail to reproduce the stimuli perceived by CI patients. It is therefore critical to develop tools capable of producing better representations of the stimuli as perceived by CI patients. Thus, this work proposes a framework that incorporates physiological models of the peripheral auditory nerve. Using these models, the framework generates stimulations that elicit a neural response at the auditory nerve closer to that observed in NH conditions. Stimulations generated by the framework were evaluated by performing a vowel identification task. However, the task was performed by a classifier trained using deep learning techniques instead of a CI patient. These results give insight into how the framework could be applied for the development and validation of CI stimulation strategies.</div>