An extended variational mode decomposition algorithm developed speech emotion recognition performance

Keyphrases

• Feature Vector 100%
• Emotion Recognition Ability 100%
• Speech Emotion Recognition 100%
• Variational Mode Decomposition Algorithm 100%
• Decomposition Parameters 100%
• Copyright 50%
• Berlin 50%
• Speech Processing 50%
• Text-dependent 50%
• Facial Expression 50%
• Classification Accuracy 50%
• Two Dimensional 50%
• Intelligent Systems 50%
• Sentiment Analysis 50%
• Tonnetz 50%
• Robust Approach 50%
• Speech Features 50%
• Emotion Recognition 50%
• Human-computer Interaction 50%
• Speech Signal 50%
• Signal Component 50%
• Synergistic Interaction 50%
• Sample Decomposition 50%
• Balance Parameter 50%
• Output Layer 50%
• VGG16 50%
• Sampling Rate 50%
• Mel-spectrogram 50%
• Chromagram 50%
• Emo-DB 50%
• Informative Features 50%
• Informative Signal 50%
• Parameter Selection 50%
• Signal Modes 50%
• Rate Parameters 50%
• Mode Decomposition Method 50%
• Emotion Recognition from Speech 50%
• Data Fidelity 50%
• Signal Sampling 50%
• Three-dimensional Features 50%
• Mel Frequency Cepstral Coefficients 50%
• Loss Components 50%
• Fidelity Constraint 50%
• VGG-16 Network 50%
• Spectral Centroid 50%

Engineering

• Speech Signal 100%
• Feature Vector 100%
• Variational Mode Decomposition 100%
• Sentiment Analysis 50%
• Output Layer 50%
• Computer Interaction 50%
• Facial Expression 50%
• Classification Accuracy 50%
• Samplerate 50%
• Mode Signal 50%
• Signal Component 50%
• Spectral Centroid 50%
• Signal Sample 50%
• Spectrogram 50%

Computer Science

• Feature Vector 100%
• Recognition Performance 100%
• Speech Emotion Recognition 100%
• Sentiment Analysis 50%
• Robust Approach 50%
• Human Computer Interaction 50%
• Classification Accuracy 50%
• Facial Expression 50%
• Decomposition Method 50%
• Dimensional Feature 50%
• Signal Component 50%
• Mel Frequency Cepstral Coefficient 50%
• Reproducibility 50%