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 621–630 of 2041 papers

Title	Date	Tasks	Status	Hype
Efficient Cross-Task Prompt Tuning for Few-Shot Conversational Emotion Recognition	Oct 23, 2023	Computational EfficiencyEmotion Recognition	—Unverified	0
Customising General Large Language Models for Specialised Emotion Recognition Tasks	Oct 22, 2023	Emotion RecognitionLanguage Modeling	—Unverified	0
MMTF-DES: A Fusion of Multimodal Transformer Models for Desire, Emotion, and Sentiment Analysis of Social Media Data	Oct 22, 2023	Emotional IntelligenceEmotion Recognition	—Unverified	0
Graph Convolutional Network with Connectivity Uncertainty for EEG-based Emotion Recognition	Oct 22, 2023	EEGEmotion Recognition	—Unverified	0
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention	Oct 20, 2023	Deep LearningEmotion Classification	CodeCode Available	0
Towards Subject Agnostic Affective Emotion Recognition	Oct 20, 2023	Domain AdaptationEEG	—Unverified	0
SALMONN: Towards Generic Hearing Abilities for Large Language Models	Oct 20, 2023	Audio captioningAutomatic Speech Recognition	CodeCode Available	3
From Multilingual Complexity to Emotional Clarity: Leveraging Commonsense to Unveil Emotions in Code-Mixed Dialogues	Oct 19, 2023	Dialogue UnderstandingEmotional Intelligence	CodeCode Available	0
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks	Oct 19, 2023	Data AugmentationEmotion Recognition	—Unverified	0
CLARA: Multilingual Contrastive Learning for Audio Representation Acquisition	Oct 18, 2023	Audio ClassificationContrastive Learning	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