Emotion recognition from speech signal using fuzzy clustering

DOI

10.2991/eusflat-19.2019.19How to use a DOI?

Keywords

Emotion recognition speech signal kmeans fuzzy clustering membership function

Abstract

Expressive speech modeling is a new trend in speech processing, including emotional speech synthesis and recognition. So far, emotion recognition from speech signal has been mainly achieved using supervised classifiers. However, clustering techniques seem well fitted to resolve such a problem, especially in huge databases, where speech labeling may be a hard and tedious task. This paper presents a novel approach for emotion recognition from speech signal, based on fuzzy clustering, including probabilistic, possibilistic and graded-possibilistic c-means. In comparison to crisp clustering, mainly using kmeans, fuzzy c-means look more fitted for this problem, and potentially offer an innovative way to analyze emotions conveyed by speech using membership degrees.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

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

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TY  - CONF
AU  - Stefano Rovetta
AU  - Zied Mnasri
AU  - Francesco Masulli
AU  - Alberto Cabri
PY  - 2019/08
DA  - 2019/08
TI  - Emotion recognition from speech signal using fuzzy clustering
BT  - Proceedings of the 11th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT 2019)
PB  - Atlantis Press
SP  - 120
EP  - 127
SN  - 2589-6644
UR  - https://doi.org/10.2991/eusflat-19.2019.19
DO  - 10.2991/eusflat-19.2019.19
ID  - Rovetta2019/08
ER  -