Emotion Recognition

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 861–870 of 2041 papers

Title	Date	Tasks	Status
Leveraging Speech PTM, Text LLM, and Emotional TTS for Speech Emotion Recognition	Sep 19, 2023	Data AugmentationEmotion Recognition	—Unverified
Watch the Speakers: A Hybrid Continuous Attribution Network for Emotion Recognition in Conversation With Emotion Disentanglement	Sep 18, 2023	DisentanglementEmotion Recognition	—Unverified
Mitigating Group Bias in Federated Learning for Heterogeneous Devices	Sep 13, 2023	Decision MakingEmotion Recognition	—Unverified
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection	Sep 13, 2023	DisentanglementEmotion Recognition	—Unverified
Overview of Memotion 3: Sentiment and Emotion Analysis of Codemixed Hinglish Memes	Sep 12, 2023	Emotion RecognitionMisinformation	—Unverified
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023	Sep 11, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Speech Emotion Recognition with Distilled Prosodic and Linguistic Affect Representations	Sep 9, 2023	Emotion RecognitionKnowledge Distillation	—Unverified
Fuzzy Fingerprinting Transformer Language-Models for Emotion Recognition in Conversations	Sep 8, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
LanSER: Language-Model Supported Speech Emotion Recognition	Sep 7, 2023	Automatic Speech RecognitionEmotion Recognition	—Unverified
Implicit Design Choices and Their Impact on Emotion Recognition Model Development and Evaluation	Sep 6, 2023	Data AugmentationEmotion Recognition	—Unverified

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All datasets Emomusic RAVDESS EMOTIC FER2013 MaSaC_ERC MPED MSP-Podcast SEED คลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	M2D-CLAP	EmoA	77.4	—	Unverified
2	M2D2	EmoA	76.7	—	Unverified
3	M2D	EmoA	76.1	—	Unverified
4	Jukebox (Pre-training: CALM)	EmoA	72.1	—	Unverified
5	CLMR (Pre-training: contrastive)	EmoA	67.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+Attention	Accuracy	86.7	—	Unverified
2	MultiMAE-DER	WAR	83.61	—	Unverified
3	Intermediate-Attention-Fusion	Accuracy	81.58	—	Unverified
4	Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedST	Accuracy	80.08	—	Unverified
5	ERANN-0-4	Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAGE	Top-3 Accuracy (%)	14.73	—	Unverified
2	FocusCLIP	Top-3 Accuracy (%)	13.73	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	VGG based	5-class test accuracy	66.13	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MaSaC-ERC-Z	F1-score (Weighted)	51.17	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	BiHDM	Accuracy	40.34	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	w2v2-L-robust-12	Concordance correlation coefficient (CCC)	0.64	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	4D-aNN	Accuracy	96.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CNN	1'"	1	—	Unverified