A Novel Decision Tree for Depression Recognition in Speech
This work addresses the problem of subjective bias in depression diagnosis for clinicians by offering an objective speech-based method, though it is incremental with limited validation.
The study tackled depression recognition in speech by proposing a new speech segment fusion method based on decision tree, resulting in recognition accuracies of 75.8% for males and 68.5% for females on gender-dependent models.
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.