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Multimodal Emotion Recognition On Iemocap 4

Metrics

Accuracy

F1

Results

Performance results of various models on this benchmark

Model Name	Accuracy	F1	Paper Title	Repository
Self-attention weight correction (A+T)	76.8	76.85	Speech Emotion Recognition Based on Self-Attention Weight Correction for Acoustic and Text Features	-
Audio + Text (Stage III)	-	70.5	HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition	-
PATHOSnet v2	80.4	78	Combining deep and unsupervised features for multilingual speech emotion recognition
MultiMAE-DER	-	-	MultiMAE-DER: Multimodal Masked Autoencoder for Dynamic Emotion Recognition
MMER	81.7	-	MMER: Multimodal Multi-task Learning for Speech Emotion Recognition
COGMEN	-	-	COGMEN: COntextualized GNN based Multimodal Emotion recognitioN
GraphSmile	86.53	-	Tracing Intricate Cues in Dialogue: Joint Graph Structure and Sentiment Dynamics for Multimodal Emotion Recognition
bc-LSTM	-	-	0/1 Deep Neural Networks via Block Coordinate Descent	-
DANN	82.7	-	Context-Dependent Domain Adversarial Neural Network for Multimodal Emotion Recognition	-
CHFusion	76.5	76.8	Multimodal Sentiment Analysis using Hierarchical Fusion with Context Modeling

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