Stephanie Gillespie1, Jacqueline Laures-Gore2, Elliot Moore1, Matthew Farina2, Scott Russell3, Benjamin Haaland4. 1. School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta. 2. Communication Disorders Program, Georgia State University, Atlanta. 3. Department of Speech-Language Pathology, Grady Memorial Hospital, Atlanta, GA. 4. Department of Population Health Sciences, University of Utah, Salt Lake City.
Abstract
Purpose: The current study aimed to identify objective acoustic measures related to affective state change in the speech of adults with post-stroke aphasia. Method: The speech of 20 post-stroke adults with aphasia was recorded during picture description and administration of the Western Aphasia Battery-Revised (Kertesz, 2006). In addition, participants completed the Self-Assessment Manikin (Bradley & Lang, 1994) and the Stress Scale (Tobii Dynavox, 1981-2016) before and after the language tasks. Speech from each participant was used to detect a change in affective state test scores between the beginning and ending speech. Results: Machine learning revealed moderate success in classifying depression, minimal success in predicting depression and stress numeric scores, and minimal success in classifying changes in affective state class between the beginning and ending speech. Conclusions: The results suggest the existence of objectively measurable aspects of speech that may be used to identify changes in acute affect from adults with aphasia. This work is exploratory and hypothesis-generating; more work will be needed to make conclusive claims. 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.
Purpose: The current study aimed to identify objective acoustic measures related to affective state change in the speech of adults with post-stroke aphasia. Method: The speech of 20 post-stroke adults with aphasia was recorded during picture description and administration of the Western Aphasia Battery-Revised (Kertesz, 2006). In addition, participants completed the Self-Assessment Manikin (Bradley & Lang, 1994) and the Stress Scale (Tobii Dynavox, 1981-2016) before and after the language tasks. Speech from each participant was used to detect a change in affective state test scores between the beginning and ending speech. Results: Machine learning revealed moderate success in classifying depression, minimal success in predicting depression and stress numeric scores, and minimal success in classifying changes in affective state class between the beginning and ending speech. Conclusions: The results suggest the existence of objectively measurable aspects of speech that may be used to identify changes in acute affect from adults with aphasia. This work is exploratory and hypothesis-generating; more work will be needed to make conclusive claims. 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.
Authors: Lu-Shih Alex Low; Namunu C Maddage; Margaret Lech; Lisa B Sheeber; Nicholas B Allen Journal: IEEE Trans Biomed Eng Date: 2010-11-11 Impact factor: 4.538
Authors: Lisa Edelkraut; Diana López-Barroso; María José Torres-Prioris; Sergio E Starkstein; Ricardo E Jorge; Jessica Aloisi; Marcelo L Berthier; Guadalupe Dávila Journal: World J Psychiatry Date: 2022-03-19