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 1991–2000 of 2041 papers

Title	Date	Tasks	Status
Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique	Jul 9, 2021	Emotion RecognitionMulti-Task Learning	—Unverified
Emotion Recognition with Machine Learning Using EEG Signals	Mar 18, 2019	BIG-bench Machine LearningEEG	—Unverified
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals	Dec 22, 2022	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling	Oct 2, 2019	Emotion Recognition	—Unverified
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context	Oct 25, 2022	EEGElectroencephalogram (EEG)	—Unverified
Emotions in the Loop: A Survey of Affective Computing for Emotional Support	May 2, 2025	ChatbotEmotion Recognition	—Unverified
Emotion Stimulus Detection in German News Headlines	Jul 27, 2021	Emotion RecognitionSentence	—Unverified
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier	Jul 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling	Jul 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Emotiphons: Emotion Markers in Conversational Speech - Comparison across Indian Languages	Dec 1, 2012	Emotion RecognitionSentiment Analysis	—Unverified

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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