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

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Showing 461–470 of 2041 papers

Title	Date	Tasks	Status	Score
Explaining (Sarcastic) Utterances to Enhance Affect Understanding in Multimodal Dialogues	Nov 20, 2022	Emotion RecognitionNatural Language Understanding	CodeCode Available	5
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations	Jun 27, 2023	Emotion Recognition	CodeCode Available	5
ExpNet: Landmark-Free, Deep, 3D Facial Expressions	Feb 2, 2018	3D Face Reconstruction3D Facial Expression Recognition	CodeCode Available	5
FEALLM: Advancing Facial Emotion Analysis in Multimodal Large Language Models with Emotional Synergy and Reasoning	May 19, 2025	Emotion Recognition	CodeCode Available	5
Affect-DML: Context-Aware One-Shot Recognition of Human Affect using Deep Metric Learning	Nov 30, 2021	Emotion RecognitionMetric Learning	CodeCode Available	5
Evaluation Metrics for Automated Typographic Poster Generation	Feb 10, 2024	Emotion Recognition	CodeCode Available	5
An experimental study in Real-time Facial Emotion Recognition on new 3RL dataset	Apr 6, 2023	Emotion RecognitionFacial Emotion Recognition	CodeCode Available	5
Crossmodal ASR Error Correction with Discrete Speech Units	May 26, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Multimodal Speech Emotion Recognition and Ambiguity Resolution	Apr 12, 2019	BIG-bench Machine LearningEmotion Recognition	CodeCode Available	5
Evaluating Gammatone Frequency Cepstral Coefficients with Neural Networks for Emotion Recognition from Speech	Jun 23, 2018	ClassificationEmotion Recognition	CodeCode Available	5

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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