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Authors: Leila Kerkeni 1 ; Youssef Serrestou 2 ; Mohamed Mbarki 3 ; Kosai Raoof 2 and Mohamed Ali Mahjoub 4

Affiliations: 1 LAUM Acoustics Laboratory of the University of Maine and LATIS Laboratory of Advanced Technologies and Intelligent Systems, France ; 2 LAUM Acoustics Laboratory of the University of Maine, France ; 3 Higher Institute of Applied Sciences and Technology of Sousse, Tunisia ; 4 LATIS Laboratory of Advanced Technologies and Intelligent Systems, Tunisia

ISBN: 978-989-758-275-2

Keyword(s): Speech Emotion Recognition, Feature Extraction, Recurrent Neural Networks, SVM, Multivariate Linear Regression, MFCC, Modulation Spectral Features.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Biomedical Engineering ; Biomedical Signal Processing ; Computational Intelligence ; Data Manipulation ; Evolutionary Computing ; Health Engineering and Technology Applications ; Human-Computer Interaction ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Methodologies and Methods ; Neural Networks ; Neurocomputing ; Neurotechnology, Electronics and Informatics ; Pattern Recognition ; Physiological Computing Systems ; Sensor Networks ; Signal Processing ; Soft Computing ; Symbolic Systems ; Theory and Methods

Abstract: In this paper we compare different approaches for emotions recognition task and we propose an efficient solution based on combination of these approaches. Recurrent neural network (RNN) classifier is used to classify seven emotions found in the Berlin and Spanish databases. Its performances are compared to Multivariate linear regression (MLR) and Support vector machine (SVM) classifiers. The explored features included: mel-frequency cepstrum coefficients (MFCC) and modulation spectral features (MSFs). Finally results for different combinations of the features and on different databases are compared and explained. The overall experimental results reveal that the feature combination of MFCC and MS has the highest accuracy rate on both Spanish emotional database using RNN classifier 90,05% and Berlin emotional database using MLR 82,41%.

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Paper citation in several formats:
Kerkeni, L.; Serrestou, Y.; Mbarki, M.; Raoof, K. and Mahjoub, M. (2018). Speech Emotion Recognition: Methods and Cases Study.In Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART, ISBN 978-989-758-275-2, pages 175-182. DOI: 10.5220/0006611601750182

@conference{icaart18,
author={Leila Kerkeni. and Youssef Serrestou. and Mohamed Mbarki. and Kosai Raoof. and Mohamed Ali Mahjoub.},
title={Speech Emotion Recognition: Methods and Cases Study},
booktitle={Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,},
year={2018},
pages={175-182},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006611601750182},
isbn={978-989-758-275-2},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Agents and Artificial Intelligence - Volume 1: ICAART,
TI - Speech Emotion Recognition: Methods and Cases Study
SN - 978-989-758-275-2
AU - Kerkeni, L.
AU - Serrestou, Y.
AU - Mbarki, M.
AU - Raoof, K.
AU - Mahjoub, M.
PY - 2018
SP - 175
EP - 182
DO - 10.5220/0006611601750182

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