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 1741–1750 of 2041 papers

Title	Date	Tasks	Status
Where is Your Evidence: Improving Fact-checking by Justification Modeling	Nov 1, 2018	Argument MiningBIG-bench Machine Learning	CodeCode Available
Deep Net Features for Complex Emotion Recognition	Oct 31, 2018	automatic-speech-translationEmotion Recognition	—Unverified
Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation	Oct 30, 2018	Domain AdaptationEmotion Recognition	—Unverified
Deep Learning as Feature Encoding for Emotion Recognition	Oct 30, 2018	Deep LearningEmotion Recognition	—Unverified
Alzheimer's Disease Diagnosis Based on Cognitive Methods in Virtual Environments and Emotions Analysis	Oct 25, 2018	EEGElectroencephalogram (EEG)	—Unverified
Learning Emotion from 100 Observations: Unexpected Robustness of Deep Learning under Strong Data Limitations	Oct 25, 2018	Emotion RecognitionWord Embeddings	—Unverified
Adversarial Machine Learning And Speech Emotion Recognition: Utilizing Generative Adversarial Networks For Robustness	Oct 24, 2018	Adversarial AttackBIG-bench Machine Learning	—Unverified
Classifying and Visualizing Emotions with Emotional DAN	Oct 23, 2018	ClassificationEmotion Classification	CodeCode Available
Embedded Deep Learning for Face Detection and Emotion Recognition with Intel© Movidius (TM) Neural Compute Stick	Oct 19, 2018	Deep LearningEmotion Recognition	—Unverified
A Multimodal Approach towards Emotion Recognition of Music using Audio and Lyrical Content	Oct 10, 2018	Emotion RecognitionWord Embeddings	—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