<abstract xmlns="http://www.w3.org/1999/xhtml"><p>The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.</p></abstract>

The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion recognition task. Among various feature selection and transformation techniques for one-stage classification, multiple classifier systems were proposed. The main idea of multiple classifiers is to arrange the emotion classification process in stages. Besides parallel and serial cases, the hierarchical arrangement of multi-stage classification is most widely used for speech emotion recognition. In this paper, we present a sequential-forward-feature-selection-based multi-stage classification scheme. The Sequential Forward Selection (SFS) and Sequential Floating Forward Selection (SFFS) techniques were employed for every stage of the multi-stage classification scheme. Experimental testing of the proposed scheme was performed using the German and Lithuanian emotional speech datasets. Sequential-feature-selection-based multi-stage classification outperformed the single-stage scheme by 12–42 % for different emotion sets. The multi-stage scheme has shown higher robustness to the growth of emotion set. The decrease in recognition rate with the increase in emotion set for multi-stage scheme was lower by 10–20 % in comparison with the single-stage case. Differences in SFS and SFFS employment for feature selection were negligible.

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Doctoral student, Vilnius University Institute of Mathematics and Informatics

Associate Professor, Vilnius Gediminas Technical University

Electrical, Control and Communication Engineering

This work is licensed under the Creative Commons Attribution 4.0 Public License.

The intensive research of speech emotion recognition introduced a huge collection of speech emotion features. Large feature sets complicate the speech emotion...

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Publié en ligne: 18 janv. 2017

Pages: 35 - 41

DOI: https://doi.org/10.1515/ecce-2016-0005

Mots clés
Classification algorithms, Emotion recognition, Human voice

© 2016 Riga Technical University

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Multi-Stage Recognition of Speech Emotion Using Sequential Forward Feature Selection

Publié en ligne: 18 janv. 2017

Pages: 35 - 41

DOI: https://doi.org/10.1515/ecce-2016-0005

Mots clésClassification algorithms, Emotion recognition, Human voice

© 2016 Riga Technical University

This work is licensed under the Creative Commons Attribution 4.0 Public License.

Mots clés
Classification algorithms, Emotion recognition, Human voice