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 231–240 of 2041 papers

Title	Date	Tasks	Status	Hype	Score
Beyond Silent Letters: Amplifying LLMs in Emotion Recognition with Vocal Nuances	Jul 31, 2024	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available	1	5
Emotion Recognition from Speech Using Wav2vec 2.0 Embeddings	Apr 8, 2021	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available	1	5
Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis	Sep 17, 2021	ArticlesEmotion Recognition	CodeCode Available	1	5
Emotion Recognition in Audio and Video Using Deep Neural Networks	Jun 15, 2020	Deep LearningEmotion Recognition	CodeCode Available	1	5
Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss	Oct 30, 2020	Emotion RecognitionWord Embeddings	CodeCode Available	1	5
MuSe 2020 -- The First International Multimodal Sentiment Analysis in Real-life Media Challenge and Workshop	Apr 30, 2020	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	1	5
Neuromorphic Event-based Facial Expression Recognition	Apr 13, 2023	Emotion RecognitionFacial Expression Recognition	CodeCode Available	1	5
Emotion Recognition on large video dataset based on Convolutional Feature Extractor and Recurrent Neural Network	Jun 19, 2020	Emotion Recognition	CodeCode Available	1	5
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction Tuning	Apr 25, 2024	Emotion ClassificationEmotion Recognition	CodeCode Available	1	5
A Transformer-based joint-encoding for Emotion Recognition and Sentiment Analysis	Jun 29, 2020	Emotion RecognitionMultimodal Sentiment Analysis	CodeCode Available	1	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