The general aim of this project is to continue the development of a reliable method for measuring chronic stuttering in the continuous speech of persons who stutter. Previous research has shown repeatedly that trained and untrained judges have exceedingly poor levels of agreement in identifying stuttering events. Furthermore, there is evidence that trained judges in different treatment/research centers show wide differences even in the total number of stuttering events that they count on identical recordings of stutterers. These findings have brought stuttering treatment research (which largely relies on observer-based real-time stuttering measures) into a state of crisis. The studies conducted in this grant seek to solve this problem by continuing the investigation of procedures that have been found to improve the reliability of perceptually-judged stuttering. These procedures are supplemented by investigations of the use of artificial neural networks (ANNs) to automatically identify intervals of speech that contain stuttering. These studies have the following specific aims: (1) to investigate the reliability (inter- and intrajudge interval-by-interval agreement) of time interval measures of stuttering in children and adults; (2) to investigate procedures for training judges to identify stuttering in real time interval units; (3) to determine the functional status of time interval measures in procedures used to treat stuttering; and (4) to continue development of ANNs for use with audio- and audiovisual-recorded speech samples by adults and children who stutter. The findings of these studies will be evaluated for their generality across stuttering research centers.
