Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization

<div>Comparisons of classification accuracies of SSER system for different emotions using the individual spectral features and the feature fusion of the proposed (TEO-Auto-Env) + spectral feature extraction techniques for EMA English database for (a) GD-male, (b) GD-female, and (c) SI case.</div>

Computational Intelligence and Neuroscience

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Figure 9

Figure 9: Stressed Speech Emotion Recognition Using Teager Energy and Spectral Feature Fusion with Feature Optimization 