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 1361–1370 of 2041 papers

Title	Date	Tasks	Status
An Attribute-Aligned Strategy for Learning Speech Representation	Jun 5, 2021	AttributeEmotion Recognition	—Unverified
Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning	Jun 4, 2021	BIG-bench Machine LearningEmotion Classification	—Unverified
EmoDNN: Understanding emotions from short texts through a deep neural network ensemble	Jun 3, 2021	Emotion RecognitionManagement	—Unverified
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models	Jun 1, 2021	Emotion Recognition	—Unverified
COIN: Conversational Interactive Networks for Emotion Recognition in Conversation	Jun 1, 2021	Emotion RecognitionEmotion Recognition in Conversation	—Unverified
Learning Paralinguistic Features from Audiobooks through Style Voice Conversion	Jun 1, 2021	Emotion RecognitionStyle Detection	—Unverified
Towards Sentiment and Emotion aided Multi-modal Speech Act Classification in Twitter	Jun 1, 2021	ClassificationEmotion Recognition	—Unverified
Emotion Recognition in Horses with Convolutional Neural Networks	May 25, 2021	Emotion Recognition	CodeCode Available
Speech & Song Emotion Recognition Using Multilayer Perceptron and Standard Vector Machine	May 19, 2021	Data AugmentationEmotion Recognition	—Unverified
MUSER: MUltimodal Stress Detection using Emotion Recognition as an Auxiliary Task	May 17, 2021	Emotion RecognitionMulti-Task Learning	—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