Multimodal Emotion Recognition On Iemocap 4

Métriques

Accuracy

F1

Résultats

Résultats de performance de divers modèles sur ce benchmark

			Paper Title
GraphSmile	86.53	-	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
DANN	82.7	-	Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition
MMER	81.7	-	MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
PATHOSnet v2	80.4	78	Combining deep and unsupervised features for multilingual speech emotion recognition
Self-attention weight correction (A+T)	76.8	76.85	Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features
CHFusion	76.5	76.8	Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling
Audio + Text (Stage III)	-	70.5	HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition
MultiMAE-DER	-	-	MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
COGMEN	-	-	COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
bc-LSTM	-	-	0/1 Deep Neural Networks via Block Coordinate Descent

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