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 1671–1680 of 2041 papers

Title	Date	Tasks	Status
CAKE: Compact and Accurate K-dimensional representation of Emotion	Jul 30, 2018	Emotion RecognitionFacial Expression Recognition (FER)	—Unverified
CALLM: Understanding Cancer Survivors' Emotions and Intervention Opportunities via Mobile Diaries and Context-Aware Language Models	Mar 12, 2025	Emotion RecognitionRAG	—Unverified
CALM: Contrastive Aligned Audio-Language Multirate and Multimodal Representations	Feb 8, 2022	Emotion RecognitionNatural Language Understanding	—Unverified
Camera-based implicit mind reading by capturing higher-order semantic dynamics of human gaze within environmental context	Jul 17, 2025	Emotion Recognition	—Unverified
CA-MHFA: A Context-Aware Multi-Head Factorized Attentive Pooling for SSL-Based Speaker Verification	Sep 23, 2024	Emotion RecognitionSelf-Supervised Learning	—Unverified
Can Emotion Fool Anti-spoofing?	May 29, 2025	Emotion RecognitionSpeech Emotion Recognition	—Unverified
CAN-GRU: a Hierarchical Model for Emotion Recognition in Dialogue	Oct 1, 2020	Emotion RecognitionOpinion Mining	—Unverified
Can We Estimate Purchase Intention Based on Zero-shot Speech Emotion Recognition?	Oct 12, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Capturing Long-term Temporal Dependencies with Convolutional Networks for Continuous Emotion Recognition	Aug 23, 2017	Emotion Recognition	—Unverified
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques	Aug 4, 2023	Emotion RecognitionSpeech 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