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 701–725 of 2041 papers

TitleStatusHype
"We care": Improving Code Mixed Speech Emotion Recognition in Customer-Care Conversations—0
Capturing Spectral and Long-term Contextual Information for Speech Emotion Recognition Using Deep Learning Techniques—0
Emo-DNA: Emotion Decoupling and Alignment Learning for Cross-Corpus Speech Emotion RecognitionCode1
Contextual Emotion Recognition Using Transformer-Based ModelsCode0
Using Scene and Semantic Features for Multi-modal Emotion Recognition—0
CFN-ESA: A Cross-Modal Fusion Network with Emotion-Shift Awareness for Dialogue Emotion RecognitionCode1
HTNet for micro-expression recognitionCode1
Unlocking the Emotional World of Visual Media: An Overview of the Science, Research, and Impact of Understanding Emotion—0
Self-Supervised Learning for Audio-Based Emotion Recognition—0
FATRER: Full-Attention Topic Regularizer for Accurate and Robust Conversational Emotion RecognitionCode0
A Change of Heart: Improving Speech Emotion Recognition through Speech-to-Text Modality ConversionCode0
Unveiling Emotions from EEG: A GRU-Based Approach—0
Cross-Corpus Multilingual Speech Emotion Recognition: Amharic vs. Other Languages—0
MASR: Multi-label Aware Speech Representation—0
Vesper: A Compact and Effective Pretrained Model for Speech Emotion RecognitionCode1
Emotional Intelligence of Large Language Models—0
Occlusion Aware Student Emotion Recognition based on Facial Action Unit Detection—0
EmoSet: A Large-scale Visual Emotion Dataset with Rich Attributes—0
Emotion recognition based on multi-modal electrophysiology multi-head attention Contrastive Learning—0
Emotion Analysis on EEG Signal Using Machine Learning and Neural Network—0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition—0
SeLiNet: Sentiment enriched Lightweight Network for Emotion Recognition in Images—0
Evaluating raw waveforms with deep learning frameworks for speech emotion recognition—0
Human Emotion Recognition Based On Galvanic Skin Response signal Feature Selection and SVM—0
A Dual-Stream Recurrence-Attention Network With Global-Local Awareness for Emotion Recognition in Textual Dialog—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