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Communication Dans Un Congrès Année : 2023

Speech Emotion Classification from Affective Dimensions: Limitation and Advantage

Résumé

In affective computing, two main paradigms are used to represent emotion: categorical representation and dimensional description in continuous space. Therefore, the emotion recognition task can be treated as a classification or regression. The main aim of this study is to investigate the relation between these two representations and propose a classification pipeline that uses only dimensional annotation. Our approach contains a neural regressor which predicts a vector of arousal, valence and dominance values for a given speech segment. This vector can be interpreted as an emotional category using a mapping algorithm. We investigate the performances of a neural network architectures, and three mapping algorithms on two corpora. Our study shows the limitation and an advantage of the emotion classification via regression approach.
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Dates et versions

hal-04239244 , version 1 (18-10-2023)

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  • HAL Id : hal-04239244 , version 1

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Meysam Shamsi. Speech Emotion Classification from Affective Dimensions: Limitation and Advantage. 11th International Conference on Affective Computing and Intelligent Interaction (ACII), MIT Media Lab, Jul 2023, Cambridge Massachusetts, USA, France. ⟨hal-04239244⟩
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