Speech emotion recognition using a fuzzy approach

This paper introduces a fuzzy approach for classifying speech emotions in which a fuzzy inference system based on fuzzy associative memory (FAM-FIS) is used for recognizing speech emotions. Experiments on two databases of emotion speech Emo-DB in German and SAVEE in English, and feature of Mel-Frequ...

Full description

Saved in:
Bibliographic Details
Published in:Journal of intelligent & fuzzy systems 2019-01, Vol.36 (2), p.1587-1597
Main Authors: Ton-That, An H., Cao, Nhan T.
Format: Article
Language:English
Subjects:
Associative memory
Bayesian analysis
Emotion recognition
Emotions
Fuzzy systems
Inference
Speech recognition
Support vector machines
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This paper introduces a fuzzy approach for classifying speech emotions in which a fuzzy inference system based on fuzzy associative memory (FAM-FIS) is used for recognizing speech emotions. Experiments on two databases of emotion speech Emo-DB in German and SAVEE in English, and feature of Mel-Frequency Cepstral Coefficients (MFCC) showed that the accuracy rates of the fuzzy inference system are better than that of Bayes and Support Vector Machine (SVM) on same kind of features and databases. Namely, with MFCC feature and 19 dimensions, Emo-DB is 74.31% and SAVEE is 97.29%.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-18594