Title: Analysis of Affective States from Vocal Acoustics in Adults with Aphasia
Dr. Elliot Moore, ECE, Chair , Advisor
Dr. Jacqueline Laures-Gore, GSU, Co-Advisor
Dr. Mark Clements, ECE
Dr. Mark Davenport, ECE
Dr. Pamela Bhatti, ECE
Dr. Bruce Walker, CoC
This research analyzed objective vocal acoustic measures of aphasic speech as they related to the detection or prediction of stress, depression, and emotional state in adults with aphasia. Assessing stress and depression in persons living with aphasia is a challenging task, often utilizing caregiver surveys or diagnostic tests which have not been modified for language difficulties. Cepstral Peak Prominence features were found to be most useful when creating an automatic classifier to detect depression. Further analysis of the feature sets suggested many of the features were moderately correlated to changes in short-term affective states. Additionally, the same feature sets used to attempt the detection and prediction of stress and depression were also used to successfully predict the presence of dysarthria in a cross-database analysis. Further work in this area could lead to automated tools to assist clinicians with their diagnoses of stress, depression, and other forms of affect in adults with aphasia.