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 661–670 of 2041 papers

Title	Date	Tasks	Status
EmoCaps:Emotion Capsule based Model for Conversationl Emotion Recognition	Mar 25, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
EMO-Codec: An In-Depth Look at Emotion Preservation capacity of Legacy and Neural Codec Models With Subjective and Objective Evaluations	Jul 22, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Emotion Analysis of Tweets Banning Education in Afghanistan	Jun 28, 2023	BenchmarkingEmotion Classification	—Unverified
EmoDiarize: Speaker Diarization and Emotion Identification from Speech Signals using Convolutional Neural Networks	Oct 19, 2023	Data AugmentationEmotion Recognition	—Unverified
Emotion Analysis on Twitter: The Hidden Challenge	May 1, 2016	BIG-bench Machine LearningEmotion Recognition	—Unverified
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble	Jun 3, 2021	Emotion RecognitionManagement	—Unverified
EEG-SCMM: Soft Contrastive Masked Modeling for Cross-Corpus EEG-Based Emotion Recognition	Aug 17, 2024	Contrastive LearningCross-corpus	—Unverified
EMOE: Modality-Specific Enhanced Dynamic Emotion Experts	Jan 1, 2025	Emotion RecognitionIntent Recognition	—Unverified
EmoFormer: A Text-Independent Speech Emotion Recognition using a Hybrid Transformer-CNN model	Jan 22, 2025	Emotion RecognitionSpeech Emotion Recognition	—Unverified
EEGminer: Discovering Interpretable Features of Brain Activity with Learnable Filters	Oct 19, 2021	EEGEeg Decoding	—Unverified

Show:10 25 50

← PrevPage 67 of 205Next →

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