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 1651–1675 of 2041 papers

TitleStatusHype
EEG based Emotion Recognition of Image Stimuli—0
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion RegressionCode0
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for ElectroencephalographyCode0
Multimodal Embeddings from Language ModelsCode0
Learning Alignment for Multimodal Emotion Recognition from SpeechCode0
Towards Multimodal Emotion Recognition in German Speech Events in Cars using Transfer Learning—0
Sentiment and Emotion Based Representations for Fake Reviews Detection—0
Emotion Recognition in Low-Resource Settings: An Evaluation of Automatic Feature Selection Methods—0
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning—0
EmotionX-IDEA: Emotion BERT -- an Affectional Model for ConversationCode0
Context-Aware Emotion Recognition NetworksCode0
Multimodal Emotion Recognition Using Deep Canonical Correlation AnalysisCode0
User independent Emotion Recognition with Residual Signal-Image Network—0
Pitch-Synchronous Single Frequency Filtering Spectrogram for Speech Emotion Recognition—0
Multiple Riemannian Manifold-valued Descriptors based Image Set Classification with Multi-Kernel Metric Learning—0
Dynamic Facial Expression Generation on Hilbert Hypersphere with Conditional Wasserstein Generative Adversarial Nets—0
Deep Temporal Analysis for Non-Acted Body Affect Recognition—0
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings—0
Comparison of Classical Machine Learning Approaches on Bangla Textual Emotion AnalysisCode0
AVEC 2019 Workshop and Challenge: State-of-Mind, Detecting Depression with AI, and Cross-Cultural Affect Recognition—0
Attending to Emotional NarrativesCode0
Towards Explainable Music Emotion Recognition: The Route via Mid-level Features—0
Multimodal Fusion with Deep Neural Networks for Audio-Video Emotion Recognition—0
EmotionX-KU: BERT-Max based Contextual Emotion ClassifierCode0
End-to-End Emotional Speech Synthesis Using Style Tokens and Semi-Supervised Training—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