Emotion Recognition from Speech: An Unsupervised Learning Approach

Authors

Stefano Rovetta^1, , Zied Mnasri^{1, 2, *,} , Francesco Masulli^1, , Alberto Cabri¹

¹DIBRIS, University of Genoa, Via Dodecaneso 35, Genova, 16146, Italy

²ENIT, University Tunis El Manar, BP37, Le Belvedere, Tunis, 1002, Tunisia

^*Corresponding author. Email: zied.mnasri@enit.utm.tn

DOI

10.2991/ijcis.d.201019.002How to use a DOI?

Keywords

Emotion recognition; Speech signal; Feature extraction; K-means; Fuzzy clustering; Membership function

Abstract

Speech processing is quickly shifting toward affective computing, that requires handling emotions and modeling expressive speech synthesis and recognition. The latter task has been so far achieved by supervised classifiers. This implies a prior labeling and data preprocessing, with a cost that increases with the size of the database, in addition to the risk of committing errors. A typical emotion recognition corpus therefore has a relatively limited number of instances. To avoid the cost of labeling, and at the same time to reduce the risk of overfitting due to lack of data, unsupervised learning seems a suitable alternative to recognize emotions from speech. The recent advances in clustering techniques make it possible to reach good performances, comparable to that obtained by classifiers, with much less preprocessing load and even with generalization guarantees. This paper presents a novel approach for emotion recognition from speech signal, based on some variants of fuzzy clustering, such as probabilistic, possibilistic and graded-possibilistic fuzzy c-means. Experiments indicate that this approach (a) is effective in recognition, with in-corpus performances comparable to other proposals in the literature but with the added value of complexity control and (b) allows an innovative way to analyze emotions conveyed by speech using possibilistic membership degrees.

Copyright

© 2021 The Authors. Published by Atlantis Press B.V.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Stefano Rovetta
AU  - Zied Mnasri
AU  - Francesco Masulli
AU  - Alberto Cabri
PY  - 2020
DA  - 2020/10/29
TI  - Emotion Recognition from Speech: An Unsupervised Learning Approach
JO  - International Journal of Computational Intelligence Systems
SP  - 23
EP  - 35
VL  - 14
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.201019.002
DO  - 10.2991/ijcis.d.201019.002
ID  - Rovetta2020
ER  -