Print Email Facebook Twitter Survey of Affect Representation Schemes used in Automatic Affect Prediction for Speech Emotion Recognition: A Systematic Review Title Survey of Affect Representation Schemes used in Automatic Affect Prediction for Speech Emotion Recognition: A Systematic Review Author Rawat, Aditi (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Dudzik, B.J.W. (mentor) Raman, C.A. (mentor) Liem, C.C.S. (graduation committee) Degree granting institution Delft University of Technology Programme Computer Science and Engineering Project CSE3000 Research Project Date 2023-06-28 Abstract Automatic affect prediction systems usually assume its underlying affect representation scheme (ARS). This systematic review aims to explore how different ARS are used for in affect prediction systems based on spoken input. The focus is only on the audio input from speakers. Various datasets for speech emotion recognition were also involved in the study to understand the motivation for certain (categorical or dimensional) schemes used for emotions. The basis, popularity, advantages and target affective states were investigated. We used Scopus and Web of Science to extract the papers, focusing on the systems in the field of Computer Science in English language. In summary, our exploration of affect representation schemes in Speech Emotion Recognition (SER) reveals a predominant focus on categorical representations of affect, particularly variations of Ekman's six basic emotions. Behavior and attitude, although rare, are also represented sometimes. Emotions like anger, happiness, and sadness receive the most attention, while the recognition of the neutral state as an emotional state remains controversial. Dimensional affect representation schemes are less common, possibly due to the difficulty in estimating valence solely from audio input. Researchers often combine multiple categorical schemes to accommodate different datasets used in SER systems, aligning the popularity of the schemes with the corresponding datasets. However, issues such as a lack of explanation for chosen categories, interchangeable use of terminology, and a weak psychological foundation for category selection pose challenges in achieving a comprehensive understanding of affect representation in SER research. Subject Affect PredictionAffect Representation SchemeSpeech Emotion Recognitionautomatic affect recognition To reference this document use: http://resolver.tudelft.nl/uuid:8d76a0b5-12f1-4fdd-9787-1c43cbbad79d Part of collection Student theses Document type bachelor thesis Rights © 2023 Aditi Rawat Files PDF Survey_of_Affect_Represen ... nition.pdf 377.95 KB Close viewer /islandora/object/uuid:8d76a0b5-12f1-4fdd-9787-1c43cbbad79d/datastream/OBJ/view