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 921–930 of 2041 papers

Title	Date	Tasks	Status
SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images	Jul 6, 2023	Emotion Recognition	—Unverified
Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM	Jul 4, 2023	Emotion Recognitionfeature selection	—Unverified
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog	Jul 2, 2023	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Empirical Interpretation of the Relationship Between Speech Acoustic Context and Emotion Recognition	Jun 30, 2023	Emotional IntelligenceEmotion Recognition	—Unverified
Emotion Analysis of Tweets Banning Education in Afghanistan	Jun 28, 2023	BenchmarkingEmotion Classification	—Unverified
Exploiting Pseudo Future Contexts for Emotion Recognition in Conversations	Jun 27, 2023	Emotion Recognition	CodeCode Available
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers	Jun 23, 2023	Emotion ClassificationEmotion Recognition	—Unverified
TACOformer:Token-channel compounded Cross Attention for Multimodal Emotion Recognition	Jun 23, 2023	EEGEmotion Recognition	—Unverified
A Comparison of Time-based Models for Multimodal Emotion Recognition	Jun 22, 2023	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
A Low-rank Matching Attention based Cross-modal Feature Fusion Method for Conversational Emotion Recognition	Jun 16, 2023	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