SOTAVerified

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 1426–1450 of 2041 papers

TitleStatusHype
Intention Analysis for Sales, Marketing and Customer Service—0
InterMulti:Multi-view Multimodal Interactions with Text-dominated Hierarchical High-order Fusion for Emotion Analysis—0
Interpretability for Multimodal Emotion Recognition using Concept Activation Vectors—0
Interpretable Concept-based Deep Learning Framework for Multimodal Human Behavior Modeling—0
Interpretable Deep Neural Networks for Dimensional and Categorical Emotion Recognition in-the-wild—0
Interpretable Deep Neural Networks for Facial Expression and Dimensional Emotion Recognition in-the-wild—0
Interpretable Emoji Prediction via Label-Wise Attention LSTMs—0
Interpretable Explainability in Facial Emotion Recognition and Gamification for Data Collection—0
Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals—0
Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction—0
Inter Subject Emotion Recognition Using Spatio-Temporal Features From EEG Signal—0
Introducing Representations of Facial Affect in Automated Multimodal Deception Detection—0
Investigating critical frequency bands and channels for EEG-based emotion recognition with deep neural networks—0
Investigating EEG-Based Functional Connectivity Patterns for Multimodal Emotion Recognition—0
Investigating Effective Speaker Property Privacy Protection in Federated Learning for Speech Emotion Recognition—0
Investigating salient representations and label Variance in Dimensional Speech Emotion Analysis—0
Investigating the Impact of Word Informativeness on Speech Emotion Recognition—0
Investigations on Audiovisual Emotion Recognition in Noisy Conditions—0
Is It Still Fair? Investigating Gender Fairness in Cross-Corpus Speech Emotion Recognition—0
A Case Study on the Independence of Speech Emotion Recognition in Bangla and English Languages using Language-Independent Prosodic Features—0
It All Matters: Reporting Accuracy, Inference Time and Power Consumption for Face Emotion Recognition on Embedded Systems—0
JEAM: A Novel Model for Cross-Domain Sentiment Classification Based on Emotion Analysis—0
JELLY: Joint Emotion Recognition and Context Reasoning with LLMs for Conversational Speech Synthesis—0
基于关系图注意力网络和宽度学习的负面情绪识别方法(Negative Emotion Recognition Method Based on Rational Graph Attention Network and Broad Learning)—0
Joint Contrastive Learning with Feature Alignment for Cross-Corpus EEG-based Emotion Recognition—0
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All datasetsEmomusicRAVDESSEMOTICFER2013MaSaC_ERCMPEDMSP-PodcastSEEDคลิปคุณพ่อให้ลูกสาวยืมโทรศัพท์และความสนุกสนาน

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1M2D-CLAPEmoA77.4—Unverified
2M2D2EmoA76.7—Unverified
3M2DEmoA76.1—Unverified
4Jukebox (Pre-training: CALM)EmoA72.1—Unverified
5CLMR (Pre-training: contrastive)EmoA67.8—Unverified
#ModelMetricClaimedVerifiedStatus
1LogisticRegression on posteriors of xlsr-Wav2Vec2.0&bi-LSTM+AttentionAccuracy86.7—Unverified
2MultiMAE-DERWAR83.61—Unverified
3Intermediate-Attention-FusionAccuracy81.58—Unverified
4Logistic Regression on posteriors of the CNN-14&biLSTM-GuidedSTAccuracy80.08—Unverified
5ERANN-0-4Accuracy74.8—Unverified
#ModelMetricClaimedVerifiedStatus
1CAGETop-3 Accuracy (%)14.73—Unverified
2FocusCLIPTop-3 Accuracy (%)13.73—Unverified
#ModelMetricClaimedVerifiedStatus
1VGG based5-class test accuracy66.13—Unverified
#ModelMetricClaimedVerifiedStatus
1MaSaC-ERC-ZF1-score (Weighted)51.17—Unverified
#ModelMetricClaimedVerifiedStatus
1BiHDMAccuracy40.34—Unverified
#ModelMetricClaimedVerifiedStatus
1w2v2-L-robust-12Concordance correlation coefficient (CCC)0.64—Unverified
#ModelMetricClaimedVerifiedStatus
14D-aNNAccuracy96.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CNN1'"1—Unverified