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 1501–1510 of 2041 papers

Title	Date	Tasks	Status
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis	Oct 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Lie-Sensor: A Live Emotion Verifier or a Licensor for Chat Applications using Emotional Intelligence	Feb 11, 2021	Emotional IntelligenceEmotion Recognition	—Unverified
LineConGraphs: Line Conversation Graphs for Effective Emotion Recognition using Graph Neural Networks	Dec 4, 2023	Emotion RecognitionGraph Attention	—Unverified
Linguistic Analysis of Sinhala YouTube Comments on Sinhala Music Videos: A Dataset Study	Jan 28, 2025	Emotion RecognitionInformation Retrieval	—Unverified
Linguistic Linked Data for Sentiment Analysis	Sep 1, 2013	Emotion RecognitionSentiment Analysis	—Unverified
LIPN-UAM at EmoInt-2017:Combination of Lexicon-based features and Sentence-level Vector Representations for Emotion Intensity Determination	Sep 1, 2017	Emotion RecognitionSentence	—Unverified
LiteLSTM Architecture Based on Weights Sharing for Recurrent Neural Networks	Jan 12, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
LLDif: Diffusion Models for Low-light Emotion Recognition	Aug 8, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
LLM supervised Pre-training for Multimodal Emotion Recognition in Conversations	Jan 20, 2025	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
LMR-CBT: Learning Modality-fused Representations with CB-Transformer for Multimodal Emotion Recognition from Unaligned Multimodal Sequences	Dec 3, 2021	Efficient Neural NetworkEmotion 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