Published in: INTERSPEECH 2017, August 20–24, 2017, Stockholm, Sweden

Authors

Pavlos Charonyktakis

Olympia Simantiraki, Anastasia Pampouchidou, Manolis Tsiknakis, Martin Cooke

Abstract

Depression is one of the most prominent mental disorders, with an increasing rate that makes it the fourth cause of disability
worldwide. The field of automated depression assessment has emerged to aid clinicians in the form of a decision support system. Such a system could assist as a pre-screening tool or even for monitoring high-risk populations. Related work most commonly involves multimodal approaches, typically combining audio and visual signals to identify depression presence and/or severity. The current study explores categorical assessment of depression using audio features alone. Specifically, since depression-related vocal characteristics impact the glottal source signal, we examine Phase Distortion Deviation which has previously been applied to the recognition of voice qualities such as hoarseness, breathiness, and creakiness, some of which are thought to be features of depressed speech. The proposed method uses as features DCT-coefficients of the Phase Distortion Deviation for each frequency band. An automated machine learning tool, Just Add Data, is used to classify speech samples. The method is evaluated on a benchmark dataset (AVEC2014), in two conditions: read-speech and spontaneous-speech. Our findings indicate that Phase Distortion Deviation is a promising audio-only feature for automated detection and assessment of the depressed speech.

Keywords

glottal source, Phase Distortion Deviation, binary classification, machine learning