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 1341–1350 of 2041 papers

Title	Date	Tasks	Status
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations	Aug 6, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Weight-based Channel-model Matrix Framework provides a reasonable solution for EEG-based cross-dataset emotion recognition	Sep 13, 2022	EEGElectroencephalogram (EEG)	—Unverified
Weighted Feature Fusion Based Emotional Recognition for Variable-length Speech using DNN	Jan 1, 2019	Emotion Recognition	—Unverified
What Does it Take to Generalize SER Model Across Datasets? A Comprehensive Benchmark	Jun 14, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Emotion analysis and detection during COVID-19	Jul 23, 2021	Domain AdaptationEmotion Recognition	—Unverified
Multimodal Magic Elevating Depression Detection with a Fusion of Text and Audio Intelligence	Jan 28, 2025	Depression DetectionEmotion Recognition	—Unverified
Who Feels What and Why? Annotation of a Literature Corpus with Semantic Roles of Emotions	Aug 1, 2018	Emotion Recognition	—Unverified
Why disentanglement-based speaker anonymization systems fail at preserving emotions?	Jan 22, 2025	DisentanglementEmotion Recognition	—Unverified
Human-Imitating Metrics for Training and Evaluating Privacy Preserving Emotion Recognition Models Using Sociolinguistic Knowledge	Apr 18, 2021	Cross-corpusEmotion Recognition	—Unverified
WikiArt Emotions: An Annotated Dataset of Emotions Evoked by Art	May 1, 2018	Emotion RecognitionImage Generation	—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