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 1501–1525 of 2041 papers

TitleStatusHype
Cross-individual Recognition of Emotions by a Dynamic Entropy based on Pattern Learning with EEG features—0
Attention Driven Fusion for Multi-Modal Emotion Recognition—0
Exploiting Vietnamese Social Media Characteristics for Textual Emotion Recognition in Vietnamese—0
The Use of AI for Thermal Emotion Recognition: A Review of Problems and Limitations in Standard Design and Data—0
A Novel Transferability Attention Neural Network Model for EEG Emotion Recognition—0
Learning Emotional-Blinded Face Representations—0
Optimizing Speech Emotion Recognition using Manta-Ray Based Feature Selection—0
Group-Level Emotion Recognition Using a Unimodal Privacy-Safe Non-Individual Approach—0
Multi-modal embeddings using multi-task learning for emotion recognition—0
Is Everything Fine, Grandma? Acoustic and Linguistic Modeling for Robust Elderly Speech Emotion RecognitionCode0
A Genetic Feature Selection Based Two-stream Neural Network for Anger Veracity Recognition—0
Fine-grained Early Frequency Attention for Deep Speaker Representation Learning—0
Convolutional Speech Recognition with Pitch and Voice Quality Features—0
Suspect AI: Vibraimage, Emotion Recognition Technology, and Algorithmic Opacity—0
Introducing Representations of Facial Affect in Automated Multimodal Deception Detection—0
Unsupervised Multi-Modal Representation Learning for Affective Computing with Multi-Corpus Wearable Data—0
EmoGraph: Capturing Emotion Correlations using Graph Networks—0
Emotion Carrier Recognition from Personal Narratives—0
Hey Human, If your Facial Emotions are Uncertain, You Should Use Bayesian Neural Networks!—0
A Transfer Learning Method for Speech Emotion Recognition from Automatic Speech Recognition—0
The BIRAFFE2 Experiment. Study in Bio-Reactions and Faces for Emotion-based Personalization for AI Systems—0
Variants of BERT, Random Forests and SVM approach for Multimodal Emotion-Target Sub-challenge—0
Emotion Correlation Mining Through Deep Learning Models on Natural Language Text—0
DSC IIT-ISM at SemEval-2020 Task 8: Bi-Fusion Techniques for Deep Meme Emotion AnalysisCode0
YNU-HPCC at SemEval-2020 Task 8: Using a Parallel-Channel Model for Memotion AnalysisCode0
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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