Training speech emotion classifier without categorical annotations

• URL: http://arxiv.org/abs/2210.07642v1
• Date: Fri, 14 Oct 2022 08:47:41 GMT
• Title: Training speech emotion classifier without categorical annotations
• Authors: Meysam Shamsi, Marie Tahon
• Abstract summary: The main aim of this study is to investigate the relation between these two representations. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm.
• Score: 1.5609988622100528
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: There are two paradigms of emotion representation, categorical labeling 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. The proposed approach contains a regressor model which is trained to predict a vector of continuous values in dimensional representation for given speech audio. The output of this model can be interpreted as an emotional category using a mapping algorithm. We investigated the performances of a combination of three feature extractors, three neural network architectures, and three mapping algorithms on two different corpora. Our study shows the advantages and limitations of the classification via regression approach.

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