Stefan Scherer,Louis‐Philippe Morency,Jonathan Gratch,John Pestian
标识
DOI:10.1109/icassp.2015.7178880
摘要
Reduced frequency range in vowel production is a well documented speech characteristic of individuals' with psychological and neurological disorders. Depression is known to influence motor control and in particular speech production. The assessment and documentation of reduced vowel space and associated perceived hypoarticulation and reduced expressivity often rely on subjective assessments. Within this work, we investigate an automatic unsupervised machine learning approach to assess a speaker's vowel space within three distinct speech corpora and compare observed vowel space measures of subjects with and without psychological conditions associated with psychological distress, namely depression, post-traumatic stress disorder (PTSD), and suicidality. Our experiments are based on recordings of over 300 individuals. The experiments show a significantly reduced vowel space in conversational speech for depression, PTSD, and suicidality. We further observe a similar trend of reduced vowel space for read speech. A possible explanation for a reduced vowel space is psychomotor retardation, a common symptom of depression that influences motor control and speech production.