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 1121–1130 of 2041 papers

Title	Date	Tasks	Status
Real-time Speech Emotion Recognition Based on Syllable-Level Feature Extraction	Apr 25, 2022	Cross-corpusEmotion Recognition	—Unverified
Recognition of Emotions using Kinects	Aug 4, 2015	Emotion Recognition	—Unverified
Recognizing Emotion Regulation Strategies from Human Behavior with Large Language Models	Aug 8, 2024	Emotion RecognitionSentiment Analysis	—Unverified
Recognizing Emotions in Video Using Multimodal DNN Feature Fusion	Jul 1, 2018	Emotion RecognitionMachine Translation	—Unverified
Recognizing More Emotions with Less Data Using Self-supervised Transfer Learning	Nov 11, 2020	Emotion RecognitionSpeech Emotion Recognition	—Unverified
Recording Brain Activity While Listening to Music Using Wearable EEG Devices Combined with Bidirectional Long Short-Term Memory Networks	Aug 22, 2024	EEGEmotion Recognition	—Unverified
Reevaluating Data Partitioning for Emotion Detection in EmoWOZ	Mar 15, 2023	Emotional IntelligenceEmotion Recognition	—Unverified
Refashioning Emotion Recognition Modelling: The Advent of Generalised Large Models	Aug 21, 2023	Emotion RecognitionFew-Shot Learning	—Unverified
ReflectDiffu:Reflect between Emotion-intent Contagion and Mimicry for Empathetic Response Generation via a RL-Diffusion Framework	Sep 16, 2024	Decision MakingDialogue Generation	—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