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Authors: Giovanni Costantini ; Valerio Cesarini and Daniele Casali

Affiliation: Department of Electronic Engineering, University of Rome Tor Vergata, Italy

Keyword(s): Emotions, Speech, Machine Learning, Arousal, Valence, Categorical, Dimensional.

Abstract: In this paper, a selection of acoustic features, derived from literature and experiments, is presented for emotion recognition. Additionally, a new speech dataset is built by recording the free speech of six subjects in a retirement home, as part of a pilot project for the care of the elder called E-Linus. The dataset is employed along with another widely used set (Emovo) for testing the effectiveness of the selected features in automatic emotion recognition. Thus, two different machine learning algorithms, namely a multi-class SVM and Naïve Bayes, are used. Due to the unbalanced and preliminary nature of the retirement home dataset, a statistical method based on logical variables is also employed on it. The 24 features prove their effectiveness by yielding sufficient accuracy results for the machine learning-based approach on the Emovo dataset. On the other hand, the proposed statistical method is the only one yielding sufficient accuracy and no noticeable bias when testing on the m ore unbalanced retirement home dataset. (More)

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Paper citation in several formats:
Costantini, G.; Cesarini, V. and Casali, D. (2022). A Subset of Acoustic Features for Machine Learning-based and Statistical Approaches in Speech Emotion Recognition. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS; ISBN 978-989-758-552-4; ISSN 2184-4305, SciTePress, pages 257-264. DOI: 10.5220/0010912500003123

@conference{biosignals22,
author={Giovanni Costantini. and Valerio Cesarini. and Daniele Casali.},
title={A Subset of Acoustic Features for Machine Learning-based and Statistical Approaches in Speech Emotion Recognition},
booktitle={Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS},
year={2022},
pages={257-264},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010912500003123},
isbn={978-989-758-552-4},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022) - BIOSIGNALS
TI - A Subset of Acoustic Features for Machine Learning-based and Statistical Approaches in Speech Emotion Recognition
SN - 978-989-758-552-4
IS - 2184-4305
AU - Costantini, G.
AU - Cesarini, V.
AU - Casali, D.
PY - 2022
SP - 257
EP - 264
DO - 10.5220/0010912500003123
PB - SciTePress