1 research outputs found
A Novel Decision Tree for Depression Recognition in Speech
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