PROJECT TITLE :

Self-Reported Symptoms of Depression and PTSD Are Associated with Reduced Vowel Space in Screening Interviews

ABSTRACT:

Reduced frequency vary in vowel production is a well documented speech characteristic of individuals with psychological and neurological disorders. Affective disorders like depression and post-traumatic stress disorder (PTSD) are known to influence motor control and in specific speech production. The assessment and documentation of reduced vowel space and reduced expressivity often either rely on subjective assessments or on analysis of speech beneath constrained laboratory conditions (e.g. sustained vowel production, reading tasks). These constraints render the analysis of such measures expensive and impractical. Inside this work, we tend to investigate an automatic unsupervised machine learning primarily based approach to assess a speaker's vowel house. Our experiments are based mostly on recordings of 253 people. Symptoms of depression and PTSD are assessed using normal self-assessment questionnaires and their cut-off scores. The experiments show a significantly reduced vowel space in subjects that scored positively on the questionnaires. We have a tendency to show the live's statistical robustness against varying demographics of people and articulation rate. The reduced vowel area for subjects with symptoms of depression can be explained by the common condition of psychomotor retardation influencing articulation and motor control. These findings could probably support treatment of affective disorders, like depression and PTSD in the future.