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 411–420 of 2041 papers

Title	Date	Tasks	Status	Score
Classifying and Visualizing Emotions with Emotional DAN	Oct 23, 2018	ClassificationEmotion Classification	CodeCode Available	5
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion Recognition	Sep 7, 2020	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Facial Emotion Recognition: A multi-task approach using deep learning	Oct 28, 2021	Deep LearningEmotion Recognition	CodeCode Available	5
JNLP at SemEval-2025 Task 11: Cross-Lingual Multi-Label Emotion Detection Using Generative Models	May 19, 2025	Emotion RecognitionMulti-Label Classification	CodeCode Available	5
Facial Emotion Recognition Under Mask Coverage Using a Data Augmentation Technique	Dec 3, 2023	Data AugmentationDecision Making	CodeCode Available	5
ABHINAYA -- A System for Speech Emotion Recognition In Naturalistic Conditions Challenge	May 23, 2025	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
Exploring Multilingual Unseen Speaker Emotion Recognition: Leveraging Co-Attention Cues in Multitask Learning	Jun 13, 2024	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	5
ExpNet: Landmark-Free, Deep, 3D Facial Expressions	Feb 2, 2018	3D Face Reconstruction3D Facial Expression Recognition	CodeCode Available	5
FeedForward at SemEval-2024 Task 10: Trigger and sentext-height enriched emotion analysis in multi-party conversations	Jun 20, 2024	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	5
ExHuBERT: Enhancing HuBERT Through Block Extension and Fine-Tuning on 37 Emotion Datasets	Jun 11, 2024	Emotion RecognitionSpeech Emotion 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