Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm

Christina Brester, Maxim Sidorov, Eugene Semenkin

2014

Abstract

In this paper the efficiency of feature selection techniques based on the evolutionary multi-objective optimization algorithm is investigated on the set of speech-based emotion recognition problems (English, German languages). Benefits of developed algorithmic schemes are demonstrated compared with Principal Component Analysis for the involved databases. Presented approaches allow not only to reduce the amount of features used by a classifier but also to improve its performance. According to the obtained results, the usage of proposed techniques might lead to increasing the emotion recognition accuracy by up to 29.37% relative improvement and reducing the number of features from 384 to 64.8 for some of the corpora.

Paper Citation

in Harvard Style

Brester C., Sidorov M. and Semenkin E. (2014). Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm . In Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014) ISBN 978-989-758-040-6, pages 851-855. DOI: 10.5220/0005148708510855

in Bibtex Style

@conference{asaahmi14,
author={Christina Brester and Maxim Sidorov and Eugene Semenkin},
title={Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm},
booktitle={Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014)},
year={2014},
pages={851-855},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005148708510855},
isbn={978-989-758-040-6},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 11th International Conference on Informatics in Control, Automation and Robotics - Volume 2: ASAAHMI, (ICINCO 2014)
TI - Acoustic Emotion Recognition - Two Ways of Features Selection based on Self-Adaptive Multi-Objective Genetic Algorithm
SN - 978-989-758-040-6
AU - Brester C.
AU - Sidorov M.
AU - Semenkin E.
PY - 2014
SP - 851
EP - 855
DO - 10.5220/0005148708510855