Speech emotion recognition using discriminative dimension reduction by employing a modified quantum-behaved particle swarm optimization algorithm

In recent years, Speech Emotion Recognition (SER) has received considerable attention in affective computing field. In this paper, an improved system for SER is proposed. In the feature extraction step, a hybrid high-dimensional rich feature vector is extracted from both speech signal and glottal-wa...

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Veröffentlicht in:	Multimedia tools and applications Jg. 79; H. 1-2; S. 1261 - 1289
Hauptverfasser:	Daneshfar, Fatemeh, Kabudian, Seyed Jahanshah
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2020 Springer Nature B.V
Schlagworte:	Acknowledgment Affective computing Algorithms Artificial neural networks Classifiers Computer Communication Networks Computer Science Data Structures and Information Theory Emotion recognition Emotions Extraction Feature extraction Function words Fundamental frequency Holistic approach Linguistics Matrix algebra Matrix methods Motion capture Multimedia Information Systems Neural networks Optimization Parameters Particle swarm optimization Probabilistic models Prosodic features Recognition Reduction Resonant frequencies Special Purpose and Application-Based Systems Speech Speech recognition Waveforms Dimension reduction Quantum-behaved particle swarm optimization Speech emotion recognition
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
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