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 1611–1620 of 2041 papers

Title	Date	Tasks	Status
Speech Emotion Recognition Using Speech Feature and Word Embedding	Nov 18, 2019	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Learning Relationships between Text, Audio, and Video via Deep Canonical Correlation for Multimodal Language Analysis	Nov 13, 2019	Emotion RecognitionMultimodal Sentiment Analysis	—Unverified
Adaptive Fusion Techniques for Multimodal Data	Nov 10, 2019	Emotion RecognitionMachine Translation	—Unverified
M3ER: Multiplicative Multimodal Emotion Recognition Using Facial, Textual, and Speech Cues	Nov 9, 2019	Emotion RecognitionMultimodal Emotion Recognition	—Unverified
Addressing Ambiguity of Emotion Labels Through Meta-Learning	Nov 6, 2019	Emotion RecognitionMeta-Learning	—Unverified
Speaker-invariant Affective Representation Learning via Adversarial Training	Nov 4, 2019	Emotion ClassificationEmotion Recognition	—Unverified
An Affective Situation Labeling System from Psychological Behaviors in Emotion Recognition	Nov 4, 2019	Emotion Recognition	—Unverified
Context-aware Interactive Attention for Multi-modal Sentiment and Emotion Analysis	Nov 1, 2019	Emotion Recognition	—Unverified
Modeling Feature Representations for Affective Speech using Generative Adversarial Networks	Oct 31, 2019	Cross-corpusEmotion Recognition	—Unverified
Privacy Enhanced Multimodal Neural Representations for Emotion Recognition	Oct 29, 2019	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