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Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach

Trnka, Marián and Darjaa, Sakhia and Ritomský, Marian and Sabo, Róbert and Rusko, Milan and Schaper, Meilin and Stelkens-Kobsch, Tim H. (2021) Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach. Electronics, Vol. 1 (23), pp. 1-16. Multidisciplinary Digital Publishing Institute (MDPI). doi: 10.3390/electronics10232950. ISSN 2079-9292.

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Official URL: https://www.mdpi.com/2079-9292/10/23/2950/htm

Abstract

A frequently used procedure to examine the relationship between categorical and dimensional descriptions of emotions is to ask subjects to place verbal expressions representing emotions in a continuous multidimensional emotional space. This work chooses a different approach. It aims at creating a system predicting the values of Activation and Valence (AV) directly from the sound of emotional speech utterances without the use of its semantic content or any other additional information. The system uses X-vectors to represent sound characteristics of the utterance and Support Vector Regressor for the estimation the AV values. The system is trained on a pool of three publicly available databases with dimensional annotation of emotions. The quality of regression is evaluated on the test sets of the same databases. Mapping of categorical emotions to the dimensional space is tested on another pool of eight categorically annotated databases. The aim of the work was to test whether in each unseen database the predicted values of Valence and Activation will place emotion-tagged utterances in the AV space in accordance with expectations based on Russell’s circumplex model of affective space. Due to the great variability of speech data, clusters of emotions create overlapping clouds. Their average location can be represented by centroids. A hypothesis on the position of these centroids is formulated and evaluated. The system’s ability to separate the emotions is evaluated by measuring the distance of the centroids. It can be concluded that the system works as expected and the positions of the clusters follow the hypothesized rules. Although the variance in individual measurements is still very high and the overlap of emotion clusters is large, it can be stated that the AV coordinates predicted by the system lead to an observable separation of the emotions in accordance with the hypothesis. Knowledge from training databases can therefore be used to predict AV coordinates of unseen data of various origins. This could be used to detect high levels of stress or depression. With the appearance of more dimensionally annotated training data, the systems predicting emotional dimensions from speech sound will become more robust and usable in practical applications in call-centers, avatars, robots, information-providing systems, security applications, and the like.

Item URL in elib:https://elib.dlr.de/146554/
Document Type:Article
Title:Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Trnka, MariánInstitute of Informatics of the Slovak Academy of Sciences, 845 07 Bratislava, SlovakiaUNSPECIFIEDUNSPECIFIED
Darjaa, SakhiaInstitute of Informatics of the Slovak Academy of Sciences, 845 07 Bratislava, SlovakiaUNSPECIFIEDUNSPECIFIED
Ritomský, MarianInstitute of Informatics of the Slovak Academy of Sciences, 845 07 Bratislava, SlovakiaUNSPECIFIEDUNSPECIFIED
Sabo, RóbertInstitute of Informatics of the Slovak Academy of Sciences, 845 07 Bratislava, SlovakiaUNSPECIFIEDUNSPECIFIED
Rusko, MilanInstitute of Informatics of the Slovak Academy of Sciences, 845 07 Bratislava, SlovakiaUNSPECIFIEDUNSPECIFIED
Schaper, MeilinUNSPECIFIEDhttps://orcid.org/0009-0003-5189-0242148087207
Stelkens-Kobsch, Tim H.UNSPECIFIEDhttps://orcid.org/0000-0002-8485-6628UNSPECIFIED
Date:29 November 2021
Journal or Publication Title:Electronics
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
Volume:Vol. 1
DOI:10.3390/electronics10232950
Page Range:pp. 1-16
Publisher:Multidisciplinary Digital Publishing Institute (MDPI)
Series Name:Special Issue Human Computer Interaction for Intelligent Systems
ISSN:2079-9292
Status:Published
Keywords:: emotion recognition; dimensional to categorical emotion representation mapping; activation; arousal and valence regression; X-vectors; SVM
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:other
DLR - Research area:Aeronautics
DLR - Program:L - no assignment
DLR - Research theme (Project):L - Managementaufgaben Luftfahrt
Location: Braunschweig
Institutes and Institutions:Institute of Flight Guidance > Controller Assistance
Institute of Flight Guidance > ATM-Simulation
Deposited By: Diederich, Kerstin
Deposited On:02 Dec 2021 10:31
Last Modified:05 Dec 2023 09:38

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