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 291–300 of 2041 papers

Title	Date	Tasks	Status	Hype
Emotionless: Privacy-Preserving Speech Analysis for Voice Assistants	Aug 9, 2019	Emotion RecognitionPrivacy Preserving	CodeCode Available	1
Contrast and Generation Make BART a Good Dialogue Emotion Recognizer	Dec 21, 2021	Contrastive LearningDecoder	CodeCode Available	1
Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings	Apr 8, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1
Emotion Recognition in Audio and Video Using Deep Neural Networks	Jun 15, 2020	Deep LearningEmotion Recognition	CodeCode Available	1
Empathetic Dialogue Generation via Sensitive Emotion Recognition and Sensible Knowledge Selection	Oct 21, 2022	Dialogue GenerationEmotion Recognition	CodeCode Available	1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation	Aug 30, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Enhancing Modal Fusion by Alignment and Label Matching for Multimodal Emotion Recognition	Aug 18, 2024	Contrastive LearningEmotion Recognition	CodeCode Available	1
Estimating the Uncertainty in Emotion Attributes using Deep Evidential Regression	Jun 11, 2023	AttributeEmotion Recognition	CodeCode Available	1
Evaluation in EEG Emotion Recognition: State-of-the-Art Review and Unified Framework	May 14, 2025	EEGEEG Emotion Recognition	CodeCode Available	1
EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech	Jun 28, 2023	Emotion RecognitionSpeech Synthesis	CodeCode Available	1

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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