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

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Showing 1751–1760 of 2041 papers

Title	Date	Tasks	Status
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
Multimodal Speech Emotion Recognition Using Audio and Text	Oct 10, 2018	Emotion ClassificationEmotion Recognition	CodeCode Available
DepecheMood++: a Bilingual Emotion Lexicon Built Through Simple Yet Powerful Techniques	Oct 8, 2018	Emotion RecognitionSentiment Analysis	CodeCode Available
Text-based Sentiment Analysis and Music Emotion Recognition	Oct 6, 2018	Emotion RecognitionMusic Emotion Recognition	—Unverified
MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations	Oct 5, 2018	Dialogue GenerationEmotion Recognition	CodeCode Available
USI-IR at IEST 2018: Sequence Modeling and Pseudo-Relevance Feedback for Implicit Emotion Detection	Oct 1, 2018	Emotion RecognitionGeneral Classification	—Unverified
CARER: Contextualized Affect Representations for Emotion Recognition	Oct 1, 2018	Emotion RecognitionSemantic Textual Similarity	CodeCode Available
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets	Oct 1, 2018	Common Sense ReasoningEmotion Classification	—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