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 1621–1630 of 2041 papers

Title	Date	Tasks	Status
Multiscale Contextual Learning for Speech Emotion Recognition in Emergency Call Center Conversations	Aug 28, 2023	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition	Oct 30, 2020	EEGElectroencephalogram (EEG)	—Unverified
Multi-Scale Temporal Transformer For Speech Emotion Recognition	Oct 1, 2024	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Multi-Source Domain Adaptation with Transformer-based Feature Generation for Subject-Independent EEG-based Emotion Recognition	Jan 4, 2024	Domain AdaptationEEG	—Unverified
Multi-Source EEG Emotion Recognition via Dynamic Contrastive Domain Adaptation	Aug 4, 2024	Domain AdaptationEEG	—Unverified
Multistage linguistic conditioning of convolutional layers for speech emotion recognition	Oct 13, 2021	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Multi-stream Attention-based BLSTM with Feature Segmentation for Speech Emotion Recognition	Oct 25, 2020	Data AugmentationEmotional Speech Synthesis	—Unverified
Affective Behavior Analysis using Action Unit Relation Graph and Multi-task Cross Attention	Jul 21, 2022	Action Unit DetectionArousal Estimation	—Unverified
Multitask Emotion Recognition Model with Knowledge Distillation and Task Discriminator	Mar 24, 2022	Emotion RecognitionKnowledge Distillation	—Unverified
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction	Jun 17, 2021	Common Sense ReasoningEmotion Cause Extraction	—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