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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 1576–1600 of 2041 papers

TitleStatusHype
Multi-face emotion detection for effective Human-Robot Interaction—0
A New Multilabel System for Automatic Music Emotion Recognition—0
Multi-Label Compound Expression Recognition: C-EXPR Database & Network—0
Multi-label Emotion Analysis in Conversation via Multimodal Knowledge Distillation—0
Multilevel Transformer For Multimodal Emotion Recognition—0
Multilingual and Multilabel Emotion Recognition using Virtual Adversarial Training—0
Multi-Lingual Sentiment Analysis of Social Data Based on Emotion-Bearing Patterns—0
Multilingual Speech Emotion Recognition With Multi-Gating Mechanism and Neural Architecture Search—0
Multi-Microphone and Multi-Modal Emotion Recognition in Reverberant Environment—0
Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture—0
Multimodal Affect Recognition using Kinect—0
Multimodal Alignment and Fusion: A Survey—0
Multimodal and Multi-view Models for Emotion Recognition—0
Multi-modal embeddings using multi-task learning for emotion recognition—0
Multimodal Emotion-Cause Pair Extraction in Conversations—0
Multimodal Emotion Recognition among Couples from Lab Settings to Daily Life using Smartwatches—0
Multimodal Emotion Recognition and Sentiment Analysis in Multi-Party Conversation Contexts—0
Multimodal Emotion Recognition based on Facial Expressions, Speech, and EEG—0
Multimodal Emotion Recognition by Fusing Video Semantic in MOOC Learning Scenarios—0
Multi-Modal Emotion Recognition by Text, Speech and Video Using Pretrained Transformers—0
Multimodal Emotion Recognition for One-Minute-Gradual Emotion Challenge—0
Multimodal Affective States Recognition Based on Multiscale CNNs and Biologically Inspired Decision Fusion Model—0
MULTI-MODAL EMOTION RECOGNITION ON IEMOCAP WITH NEURAL NETWORKS.—0
Multimodal Emotion Recognition on RAVDESS Dataset Using Transfer Learning—0
Multimodal Emotion Recognition Using Multimodal Deep Learning—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