Conference contribution


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


Publication Details
Author(s): Hönig F, Batliner A, Nöth E, Schnieder S, Krajewski J
Title edited volumes: Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
Publisher: International Speech and Communication Association
Publication year: 2014
Conference Proceedings Title: Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association (INTERSPEECH 2014)
Pages range: 1248-1252
ISSN: 1990-9772

Event details
Event: Fifteenth Annual Conference of the International Speech Communication Association (INTERSPEECH 2014)
Event location: Singapore
Start date of the event: 14/09/2014
End date of the event: 18/09/2014

Abstract

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.



How to cite
APA: Hönig, F., Batliner, A., Nöth, E., Schnieder, S., & Krajewski, J. (2014). Automatic Modelling of Depressed Speech: Relevant Features and Relevance of Gender. In Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association (INTERSPEECH 2014) (pp. 1248-1252). International Speech and Communication Association.

MLA: Hönig, Florian, et al. "Automatic Modelling of Depressed Speech: Relevant Features and Relevance of Gender." Proceedings of the Fifteenth Annual Conference of the International Speech Communication Association (INTERSPEECH 2014), Singapore International Speech and Communication Association, 2014. 1248-1252.

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