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 541–550 of 2041 papers

Title	Date	Tasks	Status
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition	Mar 13, 2025	Contrastive Learningcross-modal alignment	—Unverified
A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition	Jan 15, 2022	Emotion RecognitionLanguage Modeling	—Unverified
CocoER: Aligning Multi-Level Feature by Competition and Coordination for Emotion Recognition	Jan 1, 2025	Emotion RecognitionPseudo Label	—Unverified
A Novel Geometric Framework on Gram Matrix Trajectories for Human Behavior Understanding	Jun 29, 2018	Action RecognitionEmotion Recognition	—Unverified
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition	Sep 6, 2020	Emotion Recognitionfeature selection	—Unverified
Acoustic and linguistic representations for speech continuous emotion recognition in call center conversations	Oct 6, 2023	Emotion RecognitionTransfer Learning	—Unverified
CNN-n-GRU: end-to-end speech emotion recognition from raw waveform signal using CNNs and gated recurrent unit networks	Mar 23, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
CNN+LSTM Architecture for Speech Emotion Recognition with Data Augmentation	Feb 15, 2018	Data AugmentationEmotion Recognition	—Unverified
CNN based music emotion classification	Apr 19, 2017	ClassificationEmotion Classification	—Unverified
CN-HIT-MI.T at SemEval-2020 Task 8: Memotion Analysis Based on BERT	Dec 1, 2020	Emotion Recognition	—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