15th Annual Conference of the International Speech Communication Association

September 14-18, 2014

Automatic Modelling of Depressed Speech: Relevant Features and Relevance of Gender

Florian Hönig (1), Anton Batliner (1), Elmar Nöth (1), Sebastian Schnieder (2), Jarek Krajewski (2)

(1) FAU Erlangen-Nürnberg, Germany
(2) Universität Wuppertal, Germany

Depression is an affective disorder characterised by psychomotor retardation; in speech, this shows up in reduction of pitch (variation, range), loudness, and tempo, and in voice qualities different from those of typical modal speech. A similar reduction can be observed in sleepy speech (relaxation). In this paper, we employ a small group of acoustic features modelling prosody and spectrum that have been proven successful in the modelling of sleepy speech, enriched with voice quality features, for the modelling of depressed speech within a regression approach. This knowledge-based approach is complemented by and compared with brute-forcing and automatic feature selection. We further discuss gender differences and the contributions of (groups of) features both for the modelling of depression and across depression and sleepiness.

Full Paper

Bibliographic reference.  Hönig, Florian / Batliner, Anton / Nöth, Elmar / Schnieder, Sebastian / Krajewski, Jarek (2014): "Automatic modelling of depressed speech: relevant features and relevance of gender", In INTERSPEECH-2014, 1248-1252.