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Please use this identifier to cite or link to this item: http://hdl.handle.net/10119/16100

Title: Feature Selection Method for Real-time Speech Emotion Recognition
Authors: Elbarougy, Reda
Akagi, Masato
Keywords: Acoustic features
Feature selection
Feature reduction
Speech emotion recognition
Emotion dimensions
Real-time recognition
Fuzzy inference system
Issue Date: 2017-11-01
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Magazine name: 2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA)
Start page: 86
End page: 91
DOI: 10.1109/ICSDA.2017.8384453
Abstract: Feature selection is very important step to improve the accuracy of speech emotion recognition for many applications such as speech-to-speech translation system. Thousands of features can be extracted from speech signal however which features are the most related for speaker emotional state. Until now most of related features to emotional states are not yet found. The purpose of this paper is to propose a feature selection method which have the ability to find most related features with linear or non-linear relationship with the emotional state. Most of the previous studies used either correlation between acoustic features and emotions as for feature selection or principal component analysis (PCA) as a feature reduction method. These traditional methods does not reflect all types of relations between acoustic features and emotional state. They only can find the features which have a linear relationship. However, the relationship between any two variables can be linear, nonlinear or fuzzy. Therefore, the feature selection method should consider these kind of relationship between acoustic features and emotional state. Therefore, a feature selection method based on fuzzy inference system (FIS) was proposed. The proposed method can find all features which have any kind of above mentioned relationships. Then A FIS was used to estimate emotion dimensions valence and activations. Third FIS was used to map the values of estimated valence and activation to emotional category. The experimental results reveal that the proposed features selection method outperforms the traditional methods.
Rights: This is the author's version of the work. Copyright (C) 2017 IEEE. 2017 20th Conference of the Oriental Chapter of the International Coordinating Committee on Speech Databases and Speech I/O Systems and Assessment (O-COCOSDA), 2017, pp.86-91. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
URI: http://hdl.handle.net/10119/16100
Material Type: author
Appears in Collections:b11-1. 会議発表論文・発表資料 (Conference Papers)

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