Emotion Recognition On Ravdess
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Modellname | Accuracy | Paper Title | Repository |
|---|---|---|---|
| ERANN-0-4 | 74.8 | ERANNs: Efficient Residual Audio Neural Networks for Audio Pattern Recognition | - |
| Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST | 80.08% | Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning | - |
| LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention | 86.70% | A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset | |
| MultiMAE-DER | - | MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition | |
| Intermediate-Attention-Fusion | 81.58% | Self-attention fusion for audiovisual emotion recognition with incomplete data |
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