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 1976–2000 of 2041 papers

TitleStatusHype
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods—0
Emotion Recognition In Persian Speech Using Deep Neural Networks—0
Emotion Recognition in Speech using Cross-Modal Transfer in the Wild—0
Emotion Recognition System from Speech and Visual Information based on Convolutional Neural Networks—0
Emotion recognition techniques with rule based and machine learning approaches—0
Emotion Recognition under Consideration of the Emotion Component Process Model—0
Emotion Recognition Using Convolutional Neural Network with Selected Statistical Photoplethysmogram Features—0
Emotion Recognition Using Convolutional Neural Networks—0
Emotion Recognition Using Fusion of Audio and Video Features—0
Emotion Recognition using Machine Learning and ECG signals—0
Emotion Recognition Using Speaker Cues—0
Emotion Recognition Using Wearables: A Systematic Literature Review Work in progress—0
Emotion recognition with 4kresolution database—0
Emotion Recognition with CLIP and Sequential Learning—0
Emotion Recognition with Facial Attention and Objective Activation Functions—0
Emotion Recognition with Incomplete Labels Using Modified Multi-task Learning Technique—0
Emotion Recognition with Machine Learning Using EEG Signals—0
Emotion Recognition with Pre-Trained Transformers Using Multimodal Signals—0
Emotion Recognition with Spatial Attention and Temporal Softmax Pooling—0
Emotion Recognition With Temporarily Localized 'Emotional Events' in Naturalistic Context—0
Emotions in the Loop: A Survey of Affective Computing for Emotional Support—0
Emotion Stimulus Detection in German News Headlines—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
Emotiphons: Emotion Markers in Conversational Speech - Comparison across Indian Languages—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