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 1391–1400 of 2041 papers

Title	Date	Tasks	Status
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets	Dec 10, 2020	Deep LearningEmotion Classification	—Unverified
Multi-Classifier Interactive Learning for Ambiguous Speech Emotion Recognition	Dec 10, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
3D-CNN for Facial Emotion Recognition in Videos	Dec 7, 2020	3D Facial Expression RecognitionEmotion Recognition	—Unverified
Convolutional and Recurrent Neural Networks for Spoken Emotion Recognition	Dec 1, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Annotated Corpus of Tweets in English from Various Domains for Emotion Detection	Dec 1, 2020	Emotion Recognition	—Unverified
Technical Domain Identification using word2vec and BiLSTM	Dec 1, 2020	Classificationdomain classification	—Unverified
Contextual Augmentation of Pretrained Language Models for Emotion Recognition in Conversations	Dec 1, 2020	Emotion RecognitionLanguage Modeling	—Unverified
MEISD: A Multimodal Multi-Label Emotion, Intensity and Sentiment Dialogue Dataset for Emotion Recognition and Sentiment Analysis in Conversations	Dec 1, 2020	Emotion RecognitionSentiment Analysis	—Unverified
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer	Dec 1, 2020	Emotion RecognitionGraph Attention	—Unverified
Regrexit or not Regrexit: Aspect-based Sentiment Analysis in Polarized Contexts	Dec 1, 2020	Aspect-Based Sentiment AnalysisAspect-Based Sentiment Analysis (ABSA)	—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