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 821–830 of 2041 papers

Title	Date	Tasks	Status
End-to-end transfer learning for speaker-independent cross-language and cross-corpus speech emotion recognition	Nov 22, 2023	Cross-corpusEmotion Recognition	—Unverified
EALD-MLLM: Emotion Analysis in Long-sequential and De-identity videos with Multi-modal Large Language Model	May 1, 2024	De-identificationEmotion Recognition	—Unverified
Beyond Isolated Utterances: Conversational Emotion Recognition	Sep 13, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Are Large Language Models More Empathetic than Humans?	Jun 7, 2024	Emotion Recognition	—Unverified
An Audio-Video Deep and Transfer Learning Framework for Multimodal Emotion Recognition in the wild	Oct 7, 2020	Deep LearningEmotion Recognition	—Unverified
Context-Dependent Models for Predicting and Characterizing Facial Expressiveness	Dec 10, 2019	Emotion Recognition	—Unverified
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning	May 28, 2024	Contrastive LearningEmotion Classification	—Unverified
Enhancing Emotion Recognition in Incomplete Data: A Novel Cross-Modal Alignment, Reconstruction, and Refinement Framework	Jul 12, 2024	Contrastive Learningcross-modal alignment	—Unverified
Enhancing Facial Expression Recognition through Dual-Direction Attention Mixed Feature Networks: Application to 7th ABAW Challenge	Jul 17, 2024	Emotion RecognitionFacial Expression Recognition	—Unverified
標記對於類神經語音情緒辨識系統辨識效果之影響(Effects of Label in Neural Speech Emotion Recognition System)[In Chinese]	Oct 1, 2016	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