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 1071–1080 of 2041 papers

Title	Date	Tasks	Status
Natural Language Processing for Cognitive Analysis of Emotions	Oct 11, 2022	Emotion RecognitionManagement	—Unverified
Exploration of A Self-Supervised Speech Model: A Study on Emotional Corpora	Oct 5, 2022	Emotion Recognition	—Unverified
DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis	Oct 1, 2022	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Fine-tuning Wav2vec for Vocal-burst Emotion Recognition	Oct 1, 2022	Emotion Recognition	—Unverified
Using Extracted Emotion Cause to Improve Content-Relevance for Empathetic Conversation Generation	Oct 1, 2022	Emotion Recognition	—Unverified
基于关系图注意力网络和宽度学习的负面情绪识别方法(Negative Emotion Recognition Method Based on Rational Graph Attention Network and Broad Learning)	Oct 1, 2022	Emotion RecognitionGraph Attention	—Unverified
End-to-End Label Uncertainty Modeling in Speech Emotion Recognition using Bayesian Neural Networks and Label Distribution Learning	Sep 30, 2022	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
CEFER: A Four Facets Framework based on Context and Emotion embedded features for Implicit and Explicit Emotion Recognition	Sep 28, 2022	Emotion RecognitionSentence	—Unverified
An Application of a Runtime Epistemic Probabilistic Event Calculus to Decision-making in e-Health Systems	Sep 26, 2022	Decision MakingEmotion Recognition	—Unverified
Song Emotion Recognition: a Performance Comparison Between Audio Features and Artificial Neural Networks	Sep 24, 2022	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