Document Type
Dissertation
Disciplines
1.2 COMPUTER AND INFORMATION SCIENCE, Computer Sciences
Abstract
Major depressive disorder (MDD) is a common mental health diagnosis with estimates upwards of 25% of the United States population remain undiagnosed. Psychomotor symptoms of MDD impacts speed of control of the vocal tract, glottal source features and the rhythm of speech. Speech enables people to perceive the emotion of the speaker and MDD decreases the mood magnitudes expressed by an individual. This study asks the questions: “if high level features deigned to combine acoustic features related to emotion detection are added to glottal source features and mean response time in support vector machines and multivariate logistic regression models, would that improve the recall of the MDD class?” To answer this question, a literature review goes through common features in MDD detection, especially features related to emotion recognition. Using feature transformation, emotion recognition composite features are produced and added to glottal source features for model evaluation.
Recommended Citation
Mulligan, A. (2022). The Impact of Emotion Focused Features on SVM and MLR Models for Depression Detection. [Technological University Dublin].
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Publication Details
A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of M.Sc. in Computer Science Data Analytics 2022.