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 926–950 of 2041 papers

TitleStatusHype
Facial Emotions Recognition using Convolutional Neural Net—0
Dynamic Graph Neural ODE Network for Multi-modal Emotion Recognition in Conversation—0
Facial Expression Recognition and Image Description Generation in Vietnamese—0
Adaptive Fusion Techniques for Multimodal Data—0
Facial expression recognition based on local region specific features and support vector machines—0
Facial Geometric Feature Extraction for Dimensional Emotion Analysis Using Genetic Programming—0
FAF: A novel multimodal emotion recognition approach integrating face, body and text—0
Best Practices for Noise-Based Augmentation to Improve the Performance of Deployable Speech-Based Emotion Recognition Systems—0
Fast Facial Landmark Detection and Applications: A Survey—0
A Survey on Physiological Signal Based Emotion Recognition—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
Dynamic Causal Disentanglement Model for Dialogue Emotion Detection—0
ICON: Interactive Conversational Memory Network for Multimodal Emotion Detection—0
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition—0
BERT-ERC: Fine-tuning BERT is Enough for Emotion Recognition in Conversation—0
Feature-level and Model-level Audiovisual Fusion for Emotion Recognition in the Wild—0
Feature Selection Approaches for Optimising Music Emotion Recognition Methods—0
Feature Selection Enhancement and Feature Space Visualization for Speech-Based Emotion Recognition—0
An Architecture for Accelerated Large-Scale Inference of Transformer-Based Language Models—0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition—0
A Survey on Speech Large Language Models—0
Fusing ASR Outputs in Joint Training for Speech Emotion Recognition—0
Feelings from the Past---Adapting Affective Lexicons for Historical Emotion Analysis—0
Technical Approach for the EMI Challenge in the 8th Affective Behavior Analysis in-the-Wild Competition—0
An Approach for Improving Automatic Mouth 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