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Improving Emotion Recognition Performance by Random-Forest-Based Feature Selection

  • Olga EgorowEmail author
  • Ingo Siegert
  • Andreas Wendemuth
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11096)

Abstract

As technical systems around us aim at a more natural interaction, the task of automatic emotion recognition from speech receives an ever growing attention. One important question still remains unresolved: The definition of the most suitable features across different data types. In the present paper, we employed a random-forest based feature selection known from other research fields in order to select the most important features for three benchmark datasets. Investigating feature selection on the same corpus as well as across corpora, we achieved an increase in performance using only 40 to 60% of the features of the well-known emobase feature set.

Keywords

Speech emotion recognition Feature selection Random forest 

Notes

Acknowledgements

This work has been sponsored by the German Federal Ministry of Education and Research in the program Zwanzig20 – Partnership for Innovation as part of the research alliance 3Dsensation (grant number 03ZZ0414). It was also supported by the project Intention-based Anticipatory Interactive Systems (IAIS) funded by the European Funds for Regional Development (EFRE) and by the Federal State of Sachsen-Anhalt, Germany (grant number ZS/2017/10/88785).

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Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  1. 1.Cognitive Systems GroupOtto von Guericke UniversityMagdeburgGermany

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