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Publication . Other literature type . Article . 2021

Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach

Marian Trnka; Sakhia Darjaa; Marian Ritomský; Róbert Sabo; Milan Rusko; Meilin Schaper; Tim H. Stelkens-Kobsch;
Open Access
Published: 27 Nov 2021 Journal: Electronics, volume 10, page 2,950 (eissn: 2079-9292, Copyright policy )
Publisher: MDPI AG
Country: Germany

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.

Subjects by Vocabulary

Microsoft Academic Graph classification: Artificial intelligence business.industry business Centroid Variance (accounting) Computer science Utterance Space (commercial competition) Categorical variable Support vector machine Position (vector) Valence (psychology) Natural language processing computer.software_genre computer


Electrical and Electronic Engineering, Computer Networks and Communications, Hardware and Architecture, Signal Processing, Control and Systems Engineering, : emotion recognition, dimensional to categorical emotion representation mapping, activation, arousal and valence regression, X-vectors, SVM, emotion recognition, Electronics, TK7800-8360, emotion recognition; dimensional to categorical emotion representation mapping; activation; arousal and valence regression; X-vectors; SVM

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Funded by
Security of Air Transport Infrastructure of Europe
  • Funder: European Commission (EC)
  • Project Code: 832969
  • Funding stream: H2020 | IA
Validated by funder
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Digital Humanities and Cultural Heritage
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Other literature type . 2021