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 561–570 of 2041 papers

Title	Date	Tasks	Status	Score
Emotion Action Detection and Emotion Inference: the Task and Dataset	Mar 16, 2019	Action DetectionEmotion Classification	CodeCode Available	5
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques	Oct 8, 2018	Emotion RecognitionSentiment Analysis	CodeCode Available	5
Analysis of Self-Supervised Learning and Dimensionality Reduction Methods in Clustering-Based Active Learning for Speech Emotion Recognition	Jun 21, 2022	Active LearningClustering	CodeCode Available	5
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators	Dec 2, 2019	Dialogue Act ClassificationEmotional Dialogue Acts	CodeCode Available	5
Emotional Speech Recognition with Pre-trained Deep Visual Models	Apr 6, 2022	Emotion Recognitionspeech-recognition	CodeCode Available	5
M-MELD: A Multilingual Multi-Party Dataset for Emotion Recognition in Conversations	Mar 31, 2022	Emotion Recognition	CodeCode Available	5
Emotion Analysis in NLP: Trends, Gaps and Roadmap for Future Directions	Mar 2, 2024	Emotion Recognition	CodeCode Available	5
Deformable Convolutional LSTM for Human Body Emotion Recognition	Oct 27, 2020	Emotion Recognition	CodeCode Available	5
Complementary Fusion of Multi-Features and Multi-Modalities in Sentiment Analysis	Apr 17, 2019	Emotion RecognitionMultimodal Emotion Recognition	CodeCode Available	5
EmojiHeroVR: A Study on Facial Expression Recognition under Partial Occlusion from Head-Mounted Displays	Oct 4, 2024	Dynamic Facial Expression RecognitionEmotion 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