Speech Emotion Recognition On Ravdess
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| xlsr-Wav2Vec2.0(FineTuning) | 81.82% | A proposal for Multimodal Emotion Recognition using aural transformers and Action Units on RAVDESS dataset | |
| VQ-MAE-S-12 (Frame) + Query2Emo | 84.1 | A vector quantized masked autoencoder for speech emotion recognition | |
| CNN-14 (Fine-Tuning) | 76.58% | Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning | - |
| AlexNet (FineTuning) | 61.67% | Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning | - |
| CNN-X (Shallow CNN) | 82.99% | Shallow over Deep Neural Networks: A empirical analysis for human emotion classification using audio data | - |
0 of 5 row(s) selected.