An Investigation of Emotional Speech in Depression Classification

Brian Stasek, Julien Epps, Nicholas Cummins, Roland GOECKE

Research output: A Conference proceeding or a Chapter in BookConference contribution

14 Citations (Scopus)
2 Downloads (Pure)

Abstract

Assessing depression via speech characteristics is a growing area of interest in quantitative mental health research with a view to a clinical mental health assessment tool. As a mood disorder, depression induces changes in response to emotional stimuli, which motivates this investigation into the relationship between emotion and depression affected speech. This paper investigates how emotional information expressed in speech (i.e. arousal, valence, dominance) contributes to the classification of minimally depressed and moderately-severely depressed individuals. Experiments based on a subset of the AVEC 2014 database show that manual emotion ratings alone are discriminative of depression and combining rating-based emotion features with acoustic features improves classification between mild and severe depression. Emotion-based data selection is also shown to provide improvements in depression classification and a range of threshold methods are explored. Finally, the experiments presented demonstrate that automatically predicted emotion ratings can be incorporated into a fully automatic depression classification to produce a 5% accuracy improvement over an acoustic-only baseline system.
Original languageEnglish
Title of host publication17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5
EditorsNelson Morgan
Place of PublicationSan Francisco
PublisherInternational Speech Communication Association
Pages485-489
Number of pages5
ISBN (Print)9781510833135
DOIs
Publication statusPublished - 2016
EventInterspeech 2016 - San Francisco, San Francisco, United States
Duration: 8 Sep 201612 Sep 2016
http://www.interspeech2016.org/ (Conference website)

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ISSN (Print)2308-457X

Conference

ConferenceInterspeech 2016
CountryUnited States
CitySan Francisco
Period8/09/1612/09/16
OtherInterspeech 2016 will be organized around the topic: Understanding Speech Processing in Humans and Machines. The event will be held in the Hyatt Regency San Francisco hotel in the beautiful San Francisco, California. Interspeech 2016 emphasizes an interdisciplinary approach covering all aspects of speech science and technology spanning basic theories to applications. In addition to regular oral and poster sessions, the conference will also feature plenary talks by internationally renowned experts, tutorials, special sessions, show & tell sessions, and exhibits. A number of satellite events will take place immediately before and after the conference
Internet address

Fingerprint

Depression
Emotions
Acoustics
Mental Health
Arousal
Mood Disorders
Databases
Research

Cite this

Stasek, B., Epps, J., Cummins, N., & GOECKE, R. (2016). An Investigation of Emotional Speech in Depression Classification. In N. Morgan (Ed.), 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5 (pp. 485-489). (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH). San Francisco: International Speech Communication Association. https://doi.org/10.21437/Interspeech.2016-867
Stasek, Brian ; Epps, Julien ; Cummins, Nicholas ; GOECKE, Roland. / An Investigation of Emotional Speech in Depression Classification. 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5. editor / Nelson Morgan. San Francisco : International Speech Communication Association, 2016. pp. 485-489 (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH).
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abstract = "Assessing depression via speech characteristics is a growing area of interest in quantitative mental health research with a view to a clinical mental health assessment tool. As a mood disorder, depression induces changes in response to emotional stimuli, which motivates this investigation into the relationship between emotion and depression affected speech. This paper investigates how emotional information expressed in speech (i.e. arousal, valence, dominance) contributes to the classification of minimally depressed and moderately-severely depressed individuals. Experiments based on a subset of the AVEC 2014 database show that manual emotion ratings alone are discriminative of depression and combining rating-based emotion features with acoustic features improves classification between mild and severe depression. Emotion-based data selection is also shown to provide improvements in depression classification and a range of threshold methods are explored. Finally, the experiments presented demonstrate that automatically predicted emotion ratings can be incorporated into a fully automatic depression classification to produce a 5{\%} accuracy improvement over an acoustic-only baseline system.",
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Stasek, B, Epps, J, Cummins, N & GOECKE, R 2016, An Investigation of Emotional Speech in Depression Classification. in N Morgan (ed.), 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5. Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH, International Speech Communication Association, San Francisco, pp. 485-489, Interspeech 2016, San Francisco, United States, 8/09/16. https://doi.org/10.21437/Interspeech.2016-867

An Investigation of Emotional Speech in Depression Classification. / Stasek, Brian; Epps, Julien; Cummins, Nicholas; GOECKE, Roland.

17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5. ed. / Nelson Morgan. San Francisco : International Speech Communication Association, 2016. p. 485-489 (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH).

Research output: A Conference proceeding or a Chapter in BookConference contribution

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AU - Epps, Julien

AU - Cummins, Nicholas

AU - GOECKE, Roland

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N2 - Assessing depression via speech characteristics is a growing area of interest in quantitative mental health research with a view to a clinical mental health assessment tool. As a mood disorder, depression induces changes in response to emotional stimuli, which motivates this investigation into the relationship between emotion and depression affected speech. This paper investigates how emotional information expressed in speech (i.e. arousal, valence, dominance) contributes to the classification of minimally depressed and moderately-severely depressed individuals. Experiments based on a subset of the AVEC 2014 database show that manual emotion ratings alone are discriminative of depression and combining rating-based emotion features with acoustic features improves classification between mild and severe depression. Emotion-based data selection is also shown to provide improvements in depression classification and a range of threshold methods are explored. Finally, the experiments presented demonstrate that automatically predicted emotion ratings can be incorporated into a fully automatic depression classification to produce a 5% accuracy improvement over an acoustic-only baseline system.

AB - Assessing depression via speech characteristics is a growing area of interest in quantitative mental health research with a view to a clinical mental health assessment tool. As a mood disorder, depression induces changes in response to emotional stimuli, which motivates this investigation into the relationship between emotion and depression affected speech. This paper investigates how emotional information expressed in speech (i.e. arousal, valence, dominance) contributes to the classification of minimally depressed and moderately-severely depressed individuals. Experiments based on a subset of the AVEC 2014 database show that manual emotion ratings alone are discriminative of depression and combining rating-based emotion features with acoustic features improves classification between mild and severe depression. Emotion-based data selection is also shown to provide improvements in depression classification and a range of threshold methods are explored. Finally, the experiments presented demonstrate that automatically predicted emotion ratings can be incorporated into a fully automatic depression classification to produce a 5% accuracy improvement over an acoustic-only baseline system.

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Stasek B, Epps J, Cummins N, GOECKE R. An Investigation of Emotional Speech in Depression Classification. In Morgan N, editor, 17TH ANNUAL CONFERENCE OF THE INTERNATIONAL SPEECH COMMUNICATION ASSOCIATION (INTERSPEECH 2016), VOLS 1-5. San Francisco: International Speech Communication Association. 2016. p. 485-489. (Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH). https://doi.org/10.21437/Interspeech.2016-867