Detection of Major Depressive Disorder Based on a Combination of Voice Features: An Exploratory Approach

Abstract

In general, it is common knowledge that people’s feelings are reflected in their voice and facial expressions. This research work focuses on developing techniques for diagnosing depression based on acoustic properties of the voice. In this study, we developed a composite index of vocal acoustic properties that can be used for depression detection. Voice recordings were collected from patients undergoing outpatient treatment for major depressive disorder at a hospital or clinic following a physician’s diagnosis. Numerous features were extracted from the collected audio data using openSMILE software. Furthermore, qualitatively similar features were combined using principal component analysis. The resulting components were incorporated as parameters in a logistic regression based classifier, which achieved a diagnostic accuracy of ~90% on the training set and ~80% on the test set. Lastly, the proposed metric could serve as a new measure for evaluation of major depressive disorder.

Discrimination of mild cognitive impairment based on involuntary changes caused in voice elements

“…We have been studying techniques to diagnose depression from voice ( 11 – 13 ). Analysis using voice samples offers the advantages of noninvasiveness and easy and remote performances without the need for special, dedicated equipment.…”