Ensemble-based depression detection in speech

Abstract

Depression detection using speech signal is becoming an attractive topic because it is fast, convenient and non-invasive. Many researches aimed at improving depression classification performance. This study investigated application of ensemble learners in depression detection and compared three speaking styles (interview, reading and picture description) in ensembles. A speech dataset collecting from 184 subjects (92 depressed patients and 92 healthy controls) was used for these goals. The results showed that ensemble learners perform better than individual learners apparently. Interview is a more effective speaking style than reading and picture description for speech acquisition. These findings suggest us ensemble model based multi-utterance in interview is the best way to detect depression.