Mapping Discrete Emotions in the Dimensional Space: An Acoustic Approach
| Autor: | Sakhia Darjaa, Meilin Schaper, Marian Ritomský, Róbert Sabo, Milan Rusko, Tim H. Stelkens-Kobsch, Marian Trnka |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
X-vectors
TK7800-8360 Computer Networks and Communications Computer science SVM dimensional to categorical emotion representation mapping 050109 social psychology 02 engineering and technology Space (commercial competition) computer.software_genre emotion recognition activation arousal and valence regression Position (vector) 0202 electrical engineering electronic engineering information engineering 0501 psychology and cognitive sciences Electrical and Electronic Engineering Valence (psychology) Categorical variable business.industry 05 social sciences Centroid Variance (accounting) Support vector machine Hardware and Architecture Control and Systems Engineering Signal Processing 020201 artificial intelligence & image processing Artificial intelligence Electronics business computer Natural language processing Utterance |
| Zdroj: | Electronics, Vol 10, Iss 2950, p 2950 (2021) Electronics; Volume 10; Issue 23; Pages: 2950 |
| ISSN: | 2079-9292 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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