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 1591–1600 of 2041 papers

Title	Date	Tasks	Status
Emotion helps Sentiment: A Multi-task Model for Sentiment and Emotion Analysis	Nov 28, 2019	Emotion RecognitionSentiment Analysis	—Unverified
A Time Series Analysis of Emotional Loading in Central Bank Statements	Nov 26, 2019	Emotion RecognitionTime Series	—Unverified
Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset	Nov 22, 2019	Emotion RecognitionTime Series	CodeCode Available
MIMAMO Net: Integrating Micro- and Macro-motion for Video Emotion Recognition	Nov 21, 2019	Emotion RecognitionOptical Flow Estimation	CodeCode Available
Emotion Recognition for Vietnamese Social Media Text	Nov 21, 2019	Emotion RecognitionSentiment Analysis	—Unverified
Real-Time Emotion Recognition via Attention Gated Hierarchical Memory Network	Nov 20, 2019	Emotion RecognitionEmotion Recognition in Conversation	CodeCode Available
Take an Emotion Walk: Perceiving Emotions from Gaits Using Hierarchical Attention Pooling and Affective Mapping	Nov 20, 2019	Action RecognitionDecoder	—Unverified
Joint Emotion Label Space Modelling for Affect Lexica	Nov 20, 2019	Emotion Recognitionvalid	—Unverified
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

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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