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 1681–1690 of 2041 papers

Title	Date	Tasks	Status
Deep Learning based Emotion Recognition System Using Speech Features and Transcriptions	Jun 11, 2019	Emotion RecognitionSpeech Emotion Recognition	CodeCode Available
Focal Loss based Residual Convolutional Neural Network for Speech Emotion Recognition	Jun 11, 2019	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild	Jun 6, 2019	Emotion Recognition	—Unverified
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN	Jun 1, 2019	Emotion ClassificationEmotion Recognition	—Unverified
ParallelDots at SemEval-2019 Task 3: Domain Adaptation with feature embeddings for Contextual Emotion Analysis	Jun 1, 2019	Domain AdaptationEmotion Recognition	—Unverified
Facial Emotion Distribution Learning by Exploiting Low-Rank Label Correlations Locally	Jun 1, 2019	Emotion Recognition	—Unverified
KSU at SemEval-2019 Task 3: Hybrid Features for Emotion Recognition in Textual Conversation	Jun 1, 2019	Emotion Recognition	—Unverified
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English	May 31, 2019	Emotion ClassificationEmotion Recognition	—Unverified
A New Multilabel System for Automatic Music Emotion Recognition	May 29, 2019	BIG-bench Machine LearningEmotion Recognition	—Unverified
Two-level Explanations in Music Emotion Recognition	May 28, 2019	Emotion RecognitionMusic Emotion Recognition	—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