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
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Beschreibung
Zusammenfassung: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-waveform signal using techniques such as MFCC, PLPC, and MVDR. The prosodic features derived from fundamental frequency (f0) contour are also added to this feature vector. The proposed system is based on a holistic approach that employs a modified quantum-behaved particle swarm optimization (QPSO) algorithm (called pQPSO) to estimate both the optimal projection matrix for feature-vector dimension reduction and Gaussian Mixture Model (GMM) classifier parameters. Since the problem parameters are in a limited range and the standard QPSO algorithm performs a search in an infinite range, in this paper, the QPSO is modified in such a way that it uses a truncated probability distribution and makes the search more efficient. The system works in real-time and is evaluated on three standard emotional speech databases Berlin database of emotional speech (EMO-DB), Surrey Audio-Visual Expressed Emotion (SAVEE) and Interactive Emotional Dyadic Motion Capture (IEMOCAP). The proposed method improves the accuracy of the SER system compared to classical methods such as FA, PCA, PPCA, LDA, standard QPSO, wQPSO, and deep neural network, and also outperforms many state-of-the-art recent approaches that use the same datasets.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-019-08222-8