A Novel Decision Tree for Depression Recognition in Speech
- URL: http://arxiv.org/abs/2002.12759v1
- Date: Sat, 22 Feb 2020 10:46:38 GMT
- Title: A Novel Decision Tree for Depression Recognition in Speech
- Authors: Zhenyu Liu, Dongyu Wang, Lan Zhang and Bin Hu
- Abstract summary: This study proposes a new speech segment fusion method based on decision tree to improve the depression recognition accuracy.
The recognition accuracy are 75.8% and 68.5% for male and female respectively on gender-dependent models.
- Score: 6.487194793215743
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Depression is a common mental disorder worldwide which causes a range of
serious outcomes. The diagnosis of depression relies on patient-reported scales
and psychiatrist interview which may lead to subjective bias. In recent years,
more and more researchers are devoted to depression recognition in speech ,
which may be an effective and objective indicator. This study proposes a new
speech segment fusion method based on decision tree to improve the depression
recognition accuracy and conducts a validation on a sample of 52 subjects (23
depressed patients and 29 healthy controls). The recognition accuracy are 75.8%
and 68.5% for male and female respectively on gender-dependent models. It can
be concluded from the data that the proposed decision tree model can improve
the depression classification performance.
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