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Title: Identifying Mood Episodes Using Dialogue Features from Clinical Interviews
Bipolar disorder, a severe chronic mental illness characterized by pathological mood swings from depression to mania, requires ongoing symptom severity tracking to both guide and measure treatments that are critical for maintaining long-term health. Mental health professionals assess symptom severity through semi-structured clinical interviews. During these interviews, they observe their patients’ spoken behaviors, including both what the patients say and how they say it. In this work, we move beyond acoustic and lexical information, investigating how higher-level interactive patterns also change during mood episodes. We then perform a secondary analysis, asking if these interactive patterns, measured through dialogue features, can be used in conjunction with acoustic features to automatically recognize mood episodes. Our results show that it is beneficial to consider dialogue features when analyzing and building automated systems for predicting and monitoring mood.  more » « less
Award ID(s):
1651740
NSF-PAR ID:
10125072
Author(s) / Creator(s):
; ; ; ;
Date Published:
Journal Name:
Interspeech
Page Range / eLocation ID:
1926 to 1930
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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