School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta.
Communication Disorders Program, Georgia State University, Atlanta.
J Speech Lang Hear Res. 2018 Dec 10;61(12):2906-2916. doi: 10.1044/2018_JSLHR-S-17-0057.
The current study aimed to identify objective acoustic measures related to affective state change in the speech of adults with post-stroke aphasia.
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.
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.
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.
本研究旨在识别与脑卒中后失语症成人言语中情感状态变化相关的客观声学测量指标。
20 名脑卒中后失语症成人在进行图片描述和 Western Aphasia Battery-Revised（Kertesz，2006）测试时，其言语被记录下来。此外，参与者在语言任务前后完成了自我评估情绪量表（Bradley & Lang，1994）和应激量表（Tobii Dynavox，1981-2016）。每个参与者的演讲都用于检测演讲开始和结束时情感状态测试分数的变化。
机器学习在抑郁分类方面取得了中等程度的成功，在预测抑郁和应激数值得分方面取得了较小的成功，在分类开始和结束时言语中情感状态类别变化方面取得了较小的成功。
结果表明，言语中可能存在可客观测量的方面，可以用来识别失语症成人的急性情感变化。这项工作是探索性的和产生假说的；需要更多的工作来做出明确的结论。该领域的进一步工作可能会导致自动化工具的出现，以帮助临床医生诊断失语症成人的应激、抑郁和其他形式的情感障碍。
